Applied Artificial Intelligence: How Machine Learning Transforms How We Live and Work by TOPBOTS

APPLIED

Artificial Intelligence How Machine Learning Transforms How We Live & Work

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Contents Introduction

1. Industrials & manufacturing

4 KEY DIFFERENCES BETWEEN INDUSTRIAL AND CONSUMER AI 2. Supply chain logistics

AI IS TURNING SUPPLY CHAIN LOGISTICS INTO AUTOMATED TRADING 3. Telecommunications

SMARTER AI MEANS SAFER, SPEEDIER NETWORKS 4. Financial services

CHATBOTS GO CHA-CHING: THE LOOMING IMPACT OF AI IN FINANCE 5. Marketing & Advertising

HOW A.I. CAN SOLVE THE TOP 3 PAIN POINTS IN MARKETING

6. Beauty & retail

CAN ARTIFICIAL INTELLIGENCE GIVE YOU BEAUTY ADVICE?

7. Travel

BALANCING MACHINE LEARNING AND HUMAN INTUITION IN THE TRAVEL INDUSTRY

8. Love & dating

BITS OF LOVE: HOW AI FILLS OUR HUMAN NEED FOR INTIMACY

9. Healthcare

THE OPPORTUNITIES & CHALLENGES OF A.I. IN HEALTHCARE

10. Gaming

THE POINT OF PLAY: HOW GAMES MAKE AI SMARTER

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

INTRODUCTION “Artificial intelligence” is the buzz word of the day. You’ve no doubt read your fair share of media hype either proclaiming doom and gloom where robots seize our jobs or prophesying a new utopia where AI cures all our human problems. The reality is somewhere in between. Unless you work in the field, you might be wondering how AI is used, or even what “AI” really is. Using concrete examples and engaging storytelling, our book Applied Artificial Intelligence helps you understand how these powerful emerging technologies can be used in your own life and work. From movie recommendations on Netflix, to partner suggestions on dating apps, AI and machine learning already impact your daily digital life. Even the routes you choose to commute to work and the friends’ posts you see on Facebook or Twitter are heavily governed by sophisticated algorithms. But machine intelligence isn’t just for consumers. Industries ranging from manufacturing, logistics, telecommunications, financial services, retail, healthcare, and gaming all leverage AI for a competitive edge.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

ABOUT TOPBOTS TOPBOTS is a strategy & research firm focused on applied AI for enterprises. Our customers include leading global companies such as L’Oreal, Paypal, and WPP. We advise business leaders, executives, and practitioners on emerging technology trends and help you successfully apply them in your organization. To learn how you can adopt automation technologies and AI at your own organization, contact us at strategy@topbots.com.

Mariya Yao Head of Research & Design

HOW YOU CAN WORK WITH US • To discuss how you can adopt automation technologies and AI at your own organization, contact us at strategy@topbots.com. • To get your executive team up to speed on emerging technologies and their impact on your industry, ask us about our corporate education programs by emailing education@topbots.com. • To raise your A.I. IQ, read our publication at TOPBOTS.com or subscribe to our newsletter.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

1. Industrials & manufacturing

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

1. Industrials & manufacturing

4 KEY DIFFERENCES BETWEEN INDUSTRIAL AND CONSUMER AI

Image Credit: Shutterstock / curraheeshutter

Robots are probably the first thing you think of when asked to imagine AI applied to industrials and manufacturing. Indeed many innovative companies like Rodney Brooksâ&#x20AC;&#x2122; Rethink Robotics have developed friendly-looking robot factory workers who hustle alongside their human colleagues. Industrial robots have historically been designed to perform specific niche tasks, but modern-day robots can be taught new tasks and make real-time decisions.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

1. Industrials & manufacturing

Image Credit: Rethink Robotics

As sexy and shiny as robots are, the vast majority of the value of AI in manufacturing lies in transforming data from sensors and routine hardware into intelligent predictions for better and faster decision-making. 15 billion machines are currently connected to the Internet. By 2020, Cisco predicts the number will surpass 50 billion. Connecting these machines together into intelligent automated systems in the cloud is the next major step in the evolution of manufacturing and industry. In 2015, General Electric launched GE Digital to drive software innovation across all departments. Harel Kodesh, CTO of GE Digital, shares with us the unique challenges of applying AI to industrials that differ from consumer applications.

â&#x20AC;&#x153;When looking at the industrial internet, 40% of the data coming in is spurious and isnâ&#x20AC;&#x2122;t usefulâ&#x20AC;? - Harel Kodesh, CTO, GE Digital

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

1. Industrials & manufacturing

1. INDUSTRIAL DATA IS OFTEN INACCURATE “For machine learning to work properly, you need lots of data. Consumer data is harder to misunderstand, for example when you buy a pizza or click on an ad,” says Kodesh. “When looking at the industrial internet, however, 40% of the data coming in is spurious and isn’t useful” Let’s say you need to calculate how far a combine needs to drill and you stick a moisture sensor into the ground to take important measurements. The readings can be skewed by extreme temperatures, accidental man-handling, hardware malfunctions, or even a worm that’s been accidentally skewered by the device. “We are not generating data from the comfort and safety of a computer in your den,” Kodesh emphasizes.

2. AI RUNS ON THE EDGE, NOT ON THE CLOUD Consumer data is processed in the cloud on computing clusters with seemingly infinite capacity. Amazon can luxuriously take their time to crunch your browsing and purchase history and show you new recommendations. “In consumer predictions, there’s low value to false negatives and to false positives. You’ll forget that Amazon recommended you a crappy book,” Kodesh notes. On a deep sea oil rig, a riser is a conduit which transports oil from subsea wells to a surface facility. If a problem arises, several clamps must respond immediately to shut the valve. The sophisticated software that manages the actuators on those clamps tracks minute details in temperature and pressure. Any mistake could mean disaster. The stakes and responsiveness are much higher for industrial applications where millions of dollars and human lives can be on the line. In these cases, industrial features cannot be trusted to run on the cloud and must be implemented on location, also known as “the edge.” Industrial AI is built as an end-to-end system, described by Kodesh as a

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

1. Industrials & manufacturing

“round-trip ticket”, where data is generated by sensors on the edge, served to algorithms, modeled on the cloud, and then moved back to the edge for implementation. Between the edge and the cloud are supervisor gateways and multiple nodes of computer storage since the entire system must be able to run the the right load at the right places. In a manufacturing facility that crushes ores into platinum bars, bars that come out with the wrong consistency must be immediately detected in order to adjust the pressure at the beginning. Any delay means wasted material. Similarly, a wind turbine is constantly ingesting data to control operations. Kodesh highlights one of many possible malfunctions: “the millionth byte might be the torque on the blade, but if the torque is too high, the turbine blades will fall off. We need to serve this critical information first even if it’s in the millionth place in the queue.” Serving the correct data in real-time is a task so challenging that GE must rely on custom, in-house solutions. “Spark is fast,” admits Kodesh, “but when you make decisions in 10 milliseconds, you need different solutions.”

3. A SINGLE PREDICTION CAN COST OVER $1,000 Despite the high volume of faulty data and limited processing power at the edge, industrial AI still needs to be incredibly accurate. If an analytical system on a plane determines an engine is faulty, specialist technicians and engineers must be dispatched to remove and repair the faulty part. Simultaneously a loaner engine must be provided so the airline can keep up flight operations. The entire deal can easily surpass $200,000. “We’re not going to call and tell you there is a problem where there isn’t, and we’re definitely not going to tell you there isn’t a problem when there is,” says Kodesh. “We want to make sure we have very high fidelity systems”. According to Kodesh, the only way to ensure such high fidelity and performance

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

1. Industrials & manufacturing

is to run thousands of algorithms at the same time. A consumer company like Amazon might make $1 to $9 on a book, so they’re only willing to spend $0.001 for each user prediction. With hundreds of thousands of dollars at stake, Industrial and manufacturing giants spend between $40-$1,000 for each prediction. “For $1,000, I can run tons of algorithms in parallel, aggregate the results, and run something known as a genetic algorithm to grow the predictor,” Kodesh reveals. “This will create a survival of the fittest effect where the most fit predictors are used and less fit predictors are discarded.”

4. COMPLEX MODELS MUST BE INTERPRETABLE Consumers rarely ask why Amazon makes specific recommendations. When the stakes are higher, people ask questions. Technicians who have been in the field for 45 years will not trust machines that cannot explain their predictions. To achieve this high level of interpretability, GE needs to invent completely new technologies. Unfortunately, the talent required is painfully scarce. “I admire all the schools trying to match marketplace demand with new data scientists, but their mathematics are too shallow,” Kodesh complains. “Real data scientists need more academic depth. They literally need to be rocket scientists who know how to filter and normalize millions of data points in real-time.”

A consumer company like Amazon might make $1 to $9 on a book, so they’re only willing to spend $0.001 for each user prediction. With hundreds of thousands of dollars at stake, industrial and manufacturing giants spend between $40-$1,000 for each prediction.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

Section

2. Supply chain logistics

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

2. Supply chain logistics

AI IS TURNING SUPPLY CHAIN LOGISTICS INTO AUTOMATED TRADING

Image Credit: Shutterstock / chuyuss

Ever wonder how your Amazon Prime packages show up at your door mere hours after you place an order? A complex series of operations connects suppliers to manufacturers to wholesalers to retailers to you, the end consumer. Oversight of this process is called supply chain management (SCM). Within SCM, logistics is the portion that handles the movement of goods. E-Commerce giants like Amazon specialize in logistics while consumer packaged goods leaders like Unilever provide fullspectrum supply chain management services. Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

2. Supply chain logistics

Like every other data-driven industry, logistics and supply chain companies are investing in transformational A.I. solutions to tackle their most pressing pain points. Both small and large enterprises are dabbling in innovations ranging from machine learning to robotics. A breakdown in logistics breaks the supply chain, so companies constantly seek out improved ways to manage inventory, predict pricing, and streamline operations. Chad Lindbloom, CIO of C.H. Robinson, a Fortune 500 multi-modal transportation company, shares with us the top business use cases he’s using AI to tackle. The largest portion of C.H. Robinson’s business is North American truck freight. A portion of their customers pre-commit to regular business and outsource portions or all of their logistics needs. The remainder are one-off transactions, for which the company is a surge provider for unplanned freight. Surprisingly for a transportation company, C.H. Robinson owns no vehicles. They are instead what’s called a “freight broker”, an operational and financial middleman between buyers who want to move freight and suppliers of vehicles who can do the job. The supplier base is incredibly fragmented, ranging from one man with a truck to massive fleets of co-owned vehicles. Despite these capacity challenges, CHR must commit to move freight for a customer at a specific price in advance. Sometimes they’re asked to quote a price a last-minute same-day load. Other times they commit up to 2 years in advance.

HOW MUCH DOES A LOAD COST? Price prediction is thus their biggest business challenge. “The pricing in our industry varies seasonally, by day of the week, by lane, by time of the day,” explains Lindbloom. A “lane” is an origin destination pair, such as Toledo, OH to New York, NY. Note that reversing the lane, from NYC to Toledo, requires a different price since urban centers don’t generate high volumes of goods that must be moved back to manufacturing zones.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

2. Supply chain logistics

Many vendors such as Watson Supply Chain, ToolsGroup, and TransVoyant offer logistics and supply chain software with AI baked in, but the complexities and nuances of C.H. Robinson’s massive business require them to build in-house technology tailored to their specific needs. Pricing was previously done by human experts with deep domain experience and historical market knowledge. Prior to becoming CIO, Lindbloom spent 25 years in finance and 15 years as CFO. Combining financial with technical expertise, he and his team have built machine learning models for price prediction that resemble those built by automated traders on Wall Street. These models examine historical freight pricing data along with concurrent parameters such as the weather, traffic, and socioeconomic challenges to estimate the fair transactional price on a spot basis. AI doesn’t always outperform market experts, which Lindbloom believes will not be fully replaced. “In some cases, humans come up with better price. In most cases, the technology helps them hone in on the fair market price,” he points out. He also adds that a key benefit of effective algorithms is democratization and accessibility of information. Instead of relying on a few industry experts to produce estimates, more employees can use machine intelligence to ensure they’re quoting within market so they don’t lose the sale, and within capacity so they don’t botch the execution.

WHERE ARE ALL THE TRUCKS? The second important use case is securing and managing the supplier inventory, the vast and fragmented array of trucks available to transport loads. CHR commits to a transport price for freight buyers before they know the exact pricing and availability of requisite vehicles. The company relies on strategic human relationships, specifically a vast trading network across employees to find the right truck with the right capacity for the load.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

2. Supply chain logistics

For every lane, CHR runs background analytics to examine which carriers have moved freight at what price and service level. Fragile, expensive, or timesensitive freight requires a much higher service level. Pooling together these various factors allows CHR to optimize matching freight to the best mover.

CAN WE EXPECT THE UNEXPECTED? Managing disruptions is the third important business task that can be improved with AI. Hurricanes, carrier bankruptcies, and employee strikes all have the potential to cause massive damages to the logistics business. Predicting such disruptions and training AI to learn from contingency plans developed by humans enables automated corrective action in the future. To do so, CHR pulls together sources of information to analyze the impact of past disruptions, such as a carrier strike in France or a hurricane in the NE United States. If a distribution center is threatened with adverse weather, freight can be re-routed to a safer one. Part of the data collection entails detailed surveys that track how human employees handled disruptions and the outcomes of their management. Lindbloom hopes that eventually systems can be trained to automatically take optimal actions after learning from humans.

HOW DO WE BUILD THE TECHNOLOGY? “We are constantly looking at what’s on the marketplace, and we believe we build better technology,” says Lindbloom. Due to a critical need for reliability, CHR builds and manages their own data centers, only going to the cloud if extra computing power is needed. Owning data center resources allows CHR to spin up environments very quickly as needed, but also to commit idle systems to research and development.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

2. Supply chain logistics

In addition to flexibility, owning data centers enables privacy and control. “We are a cloud provider of transportation management system to our customers,” emphasizes Lindbloom. “We have all the same technology as the core cloud providers, but we know where all the data is, we can control it, and we make confidentiality promises to customers. Many of them are more comfortable Image Credit: Shutterstock / MAGNIFIER

“We are a cloud provider of transportation management system to our customers. We have all the same technology as the core cloud providers, but we know where all the data is, we can control it, and we make confidentiality promises to customers. Many of them are more comfortable using us” - Chad Lindbloom, CIO, C.H. Robinson

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

2. Supply chain logistics

using us.” “Technology is such a differentiating factor in our industry,” Lindbloom concludes. Other giants in logistics and supply chain agree and also committed substantial dollars to AI initiatives. DHL aims to reduce costs with autonomous cars, Active Ants builds wearable technology to optimize warehouse tasks, Locus Robotics develops warehouse robots, and Honda leverages smartphone applications for real-time shipment tracking.

WHERE WILL WE GO FROM HERE? DHL’s 2016 Logistics Trend Radar predicts that artificial intelligence investments will continue to surge for both domestic and international logistics. Increasingly more companies plan to invest in in-house development for AI applications in predictive analytics, operations and management, augmented reality, robotics, and industrial IoT. Lindbloom has words of wisdom for those who want to replicate CHR’s success with AI: “Many of the things you’re going to try probably won’t produce value. Be willing to experiment and fail fast. Try to solve the same questions with multiple different models. Multivariate-type testing is key.” Additionally, he cautions against overfocusing on AI and encourages executives to define clear business use cases first. “Have the business challenges drive your development, instead of data scientists and engineers pushing AI into the business.”

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

Section

3. Telecommunications

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

3. Telecommunications

SMARTER AI MEANS SAFER, SPEEDIER NETWORKS

Image Credit: Shutterstock / noolwlee

With more than 12,500 patents, 8 Nobel prizes, and a 140 year history of field-testing crazy ideas, no one should be surprised that AT&T would be an important player in artificial intelligence. â&#x20AC;&#x153;AT&T is a backbone of the internet,â&#x20AC;? explains Nadia Morris, Head of Innovation at the AT&T Connected Health Foundry. The company manages wireless, landline, and even private secure networks to power connectivity for both individuals and corporations. All these networks generate incredible volumes of data ripe for machine analysis.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

3. Telecommunications

AT&T has built AI and machine learning systems for decades, using algorithms to automate operations such as common call center procedures and the analysis and correction of network outages. On the entertainment side, AT&T’s DirecTV division leverages users’ rating histories, viewing behaviors, and other factors to anticipate the next films they’ll watch.

AI & DATA VISUALIZATION HELP OPTIMIZE 5G ROLLOUTS Modern A.I. algorithms have enabled the telecom company to tackle even more complex tasks, such as optimizing the rollout of their 5G network. Traditional cell towers are usually suboptimally placed near urban centers and form an imperfect grid, leading to gaps in coverage. They’re also expensive to put up and maintain and incur challenges with real estate and property ownership. Small cells are less expensive, more compact cells that can be installed on inner city buildings on a much finer grid. Their role is to repeat the signal from the main cell towers to get closer to end users. By crunching mobile subscriber data, well-calibrated AI can help create spatial models to hone in on ideal spots to build small cells to ensure maximum 5G signal strength for customers. Designing the right 5G infrastructure is critical, especially given the rapid rise of video. “Video is more than half of our mobile traffic,” explains Chris Volinsky, who leads big data research at AT&T Labs. “Video traffic grew over 75% and smartphones drove almost 75% of our data traffic in 2016 alone. We expect video traffic growth to outpace overall data growth in 2020”. Infrastructure is an enormous investment, even with small cells, so accurately modeling trends and usage growth is key to success. Demographic trends can cause previously underutilized areas to suddenly become hot traffic generators. While statistical models are useful for identifying trends in customer movement and throughout, AI and machine learning techniques create future projections from current data.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

3. Telecommunications

“We need to visualize billions of data points in a spatiotemporal fashion,” Volinsky elaborates. No tools existed previously to address AT&T’s unique data challenges, so they built and open-source custom tools such as Nanocubes, a data visualization tool that can map out millions of connections of individual mobile phones and connected devices to cell phone towers. The tool has been used outside the company to characterize sports fans in real time and analyze crime rates and history.

Image Credit: Shutterstock / AT&T

Algorithms and tools are not the bottleneck in solving problems. Volinsky clarifies that “the challenge is in the data and the data pipeline”. Modern datahungry AI approaches require a centralized data source, but gathering one across a myriad of networks with idiosyncratic standards is no trivial task. Each small cell collects cellular data differently. Some track 4G but not 3G. Some don’t get iPhone data. If variations are not taken into account, bias will appear in the data and the results. “There is no world expert in data munging,” Volinsky bemoans. “To succeed, you have to figure out organizationally how to access data in different silos, technically how to integrate with it, and ensure the formats are in line.” Data scientists often discover that they can’t solve the problems they want to solve because the fundamentals of managing data is difficult and time-consuming. “This is not the stuff people learn in grad school,” he warns.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

3. Telecommunications

OLDER METHODS CAN BE JUST AS USEFUL AS NEW ONES Volinsky’s convinced that AI is the most powerful addition in the toolbox used by AT&T’s research arm to develop the next generation of enterprise and consumer-facing solutions. At the same time, he cautions against using deep learning as a magical black box to solve all problems. Instead, you should prioritize solid data infrastructure, subject matter expertise, and utilizing an ensemble of methods from data science and machine learning toolboxes. Volinsky would know best. His BellKor team won the coveted $1 Million Netflix Prize in 2009. The key lesson learned during the three year competition was the power of ensembles. Ensembles involve combining various methods – ranging from regression, support vector machines, singular value decomposition, restricted boltzmann machines, and neural networks – to produce a result. “Deep learning is a power tool in your toolbox, but you still need your old school tools to solve problems,” he emphasizes. “Deep learning evangelists say neural networks effectively incorporate all the other models, but I have not seen that work in practice.”

ECONOMY OF SCALE HELPS STARTUPS & HOSPITALS In tandem with in-house projects, AT&T operates six innovation labs, called Foundries, all over the world. Each Foundry specializes in a different industry. As Head of Innovation at AT&T’s Connected Health Foundry, Nadia Morris works with aspirational startups such as AIRA, a smart wearables startup that uses human-assisted computer vision algorithms to enable the blind and vision-impaired to “visualize” their surroundings and navigate their immediate environment. Using established manufacturing relationships, AT&T helps healthcare IoT and wearables companies like AIRA accelerate their hardware prototyping and production. Similar to the Labs, the Foundries also leverage custom-built opensource tools such as Flow Designer, a rapid prototyping tool that simplifies hardware design for software engineers.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

3. Telecommunications

Remember Morris’ earlier comment about how the internet runs on AT&T? Turns out this can be mission critical for startups like AIRA which must ensure superior connectivity at all times to protect the safety of their patients. Since AT&T’s AI systems regulate network traffic, they can intelligently detect AIRA devices on their network and dynamically allocate greater bandwidth to support live video streaming. AT&T’s control of networks also comes in useful for hospitals who hoard sensitive patient data. Fearful of security lapses, many operate their own data centers for fear of uploading personal information to the cloud. Data center management is typically not a hospital’s core competency, leading to outdated technology and massive inefficiencies. “Do you want to run a hospital or do you want to run a data center,” questions Morris. Regardless of the cloud provider a hospital chooses to use, AT&T runs private network connections to all of their servers. “This traffic will never traverse the public internet,” she assures, giving hospitals an extra layer of protection. Migrating more hospitals to the cloud solves not only administrative pains, but also unblocks AI research. “Hospitals are smart, but they’re like islands,” Morris explains. Competition often incentivizes hospitals to hoard data that is critical to share for superior results. Pooling hospital data into “collaborative cloud communities” and applying de-identification protocols enables medical researchers to access disparate data sets with greater geographic diversity. Algorithms for essential patient services such as vital sign monitoring can be trained on aggregate data sets for more accurate benchmarks.

HUMANS WORK WITH BOTS TO ENSURE CUSTOMER SUCCESS Lead Inventive Scientist Wen-Ling Hsu has been with AT&T for over 20 years. She obsessed over creating amazing customer experiences using massive data and information even before “big data” was coined.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

3. Telecommunications

Hsu analyzes customer conversations from both phone conversations from call centers and online chats with support agents. Machine learning allows her to build textual models, identify customer intent, and route them to appropriate support agents faster. With her extensive experience, Hsu learned that interpreting and using the intelligence gained from AI systems is “more of an art than a science.” What matters most is customer perception and seamless execution, so Hsu employs a combination of bots that directly interact with customers and those that stay in the background to assist human agents. When asked to make a forecast for AI in 2017, Hsu responded, “Human judgment still plays a critical role in many tasks. Together, AI bots and human agents can learn from every customer interaction to personalize the customer experience.”

Deep learning is a power tool in your toolbox, but you still need your old school tools to solve problems. Deep learning evangelists say neural networks effectively incorporate all the other models, but I have not seen that work in practice. - Chris Volinsky, Assistant Vice President, Big Data Research, AT&T

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

Section

4. Financial services

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

4. Financial services

CHATBOTS GO CHA-CHING: THE LOOMING IMPACT OF AI IN FINANCE

Image Credit: Shutterstock / ImYanis

From minting coins to dispensing greenbacks on ATMs, the love affair between money and machine goes a long way back. The pervasive influence of technology in how we create, exchange and store money treads a colorful history â&#x20AC;&#x201D; replete with culture-shifting innovations such as cash registers, magnetic credit cards and mobile wallets.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

4. Financial services

The romance is far from over. With recent advances in machine learning, computer vision, voice recognition, natural language processing and other areas of artificial intelligence, the chemistry between money and machine is just warming up. Accenture recently reported that the vast majority of industry insiders believe AI will become the “primary” channel through which banks and their customers will interact within the next three years. Tell-tale signs are all over the place: Capital One recently launched an NLP-capable chatbot named Eno at the wake of other industry firsts in terms of AI applications. Eno enables customers to chat with the bank using text-based natural language to pay bills and retrieve account information. Among the pioneering financial institutions to join the IoT bandwagon, Capital One also launched an Alexa Skill for Amazon Echo and plans to be the first to launch a similar service for Microsoft’s Cortana. Mastercard is leveraging AI to enhance experiences across its ecosystem (for consumers, partners, issuers, and merchants), as well as to hike operational efficiency and close loopholes in areas such as fraud and false declines. The financial services giant worked with AI enabler Kasisto to build chatbots for banks and merchants to better engage consumers. Taking a step further, Mastercard also partnered with IBM’s Watson and General Motors to explore scenarios where users can “safely” conduct commerce even while on the road (such as pre-ordering their favorite cup of coffee and picking it up from the drive-through counter on their way to the office).

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

4. Financial services

Thomson Reuters has used complex algorithms for years to organize their humongous stores of information and generate time-critical financial market data for institutional clients. Their use of AI centers on improving the experience of knowledge workers by building systems that not only answer questions on structured data (e.g., give me all age discrimination cases in California in the last four years) but also provide proactive insights for decision makers (e.g., making a comparative macroeconomic analysis of two countries or showing differences and trade offs between two investment options). As a leading content publisher, Thomson Reuters also uses machine learning and AI to detect and identify fake news. The Reuters News Tracer leverages an algorithm that looks at more than 700 factors to determine whether a trending topic on social media is factual or not. Wells Fargo leveraged machine learning originally to prevent fraud, but AI now permeates other areas including compliance, customer experience, underwriting and authentication. The San Francisco-based financial institution is also exploring the use of consumer-facing virtual assistants for information updates, improvements in transaction capabilities and business insights. Bipin Sahni, EVP and Head of Innovation and R&D, reports that “regularly collaborating with startups on a wide range of technologies helps us explore big ideas outside our walls.” After unleashing Watson on Jeopardy and offering it as an AI platform for companies in the healthcare, travel and other industries, IBM retrofitted its question-answering and NLP-capable brainchild for the financial sector in December 2016. Beta-launched as IBM Watson for Cybersecurity, the program already attracted dozens of clients, including key players in the insurance and banking industries. As reported by Business Insider, global firms such as Sumitomo Mitsui Banking Corp. and Sun Financial will help test Watson’s ability to identify and combat cyberattacks. In addition to accurately detecting suspicious behavior, Watson’s machine learning component will help improve its ability to interpret and analyze cybersecurity data over time.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

4. Financial services

Even personal finance and wealth management have come to favor artificial intelligence, as shown by the rapid spread of AI-driven mobile apps, the comparatively higher success rates of quantitative trading and the cost-efficiency of robo advisors. Robo advisors such as Wealthfront and WiseBanyan are automated portfolio management services that use computer algorithms to manage and grow customer investments. Banks and other financial institutions have been using computer automation to improve operational efficiency for decades. With big data and AI technologies on the uptrend, the development of smarter, better and more powerful automations will profoundly transform the industry. From providing regulatory compliance requirements to expediting reports generation, AI will become a pervasive force across all the components of a financial organization.

THE HUMAN MINDS BEHIND ARTIFICIAL INTELLIGENCE TOPBOTS interviewed key industry leaders to make sense of the profound changes triggered by different applications of artificial intelligence in the world of finance. These influencers illuminated the details of how AI research and development work in their respective organizations. For Margaret Mayer, VP of Software Engineering at Capital One, customer needs and feedback set the tone for any AI initiative at the company. She said, “We use analytics in general to look for customer patterns, whether that be spending patterns or concerns about fraud.” Mayer’s colleague, AI Design Head Steph Hay believes context is the biggest design challenge. Hay described the importance of context as the sweet spot between design and technology. According to Hay, AI developers often shift between two opposite directions: one that leverages the company’s massive datasets to build a common template for customer interactions; and another that needs to be resolved as a unique scenario involving a single customer and the bank. She emphasized her focus on how to make “better assumptions and provide smarter responses based on everyone’s data, not just your data.”

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

4. Financial services

Meanwhile, Thomson Reuters’ VP of Research and Development Khalid AlKofahi has been a domain expert for his entire career and seeks to demystify the buzz around AI. He recounted the evolution of AI at the company as programmatic functions driven by analytics and designed to establish a specific user experience. For Al-Kofahi, the most difficult thing in AI development is “finding the right problem and scoping it right.” Otherwise, talent, time and other organizational resources can easily be wasted. Mastercard’s forays into AI-focused research and partnerships stem from its corporate mandate to “enable commerce across every device and environment,” according to Kiki Del Valle who serves as SVP at the firm’s Commerce for Every Device unit. Scheduled for a year-end rollout, the company’s joint project with IBM and General Motors will use voice recognition technology and NLP to locate nearby establishments and make quick, effortless transactions. While AI has tremendous potential, Del Valle cautions organizations against over exposure: “Unless it’s solving a problem or addressing a specific need, then don’t jump into AI for the sake of it,” citing the crucial importance of scalability from the point of view of issuers, merchants and other stakeholders. Wells Fargo currently works with promising AI startup Kasisto to enhance customer experiences via a conversational AI service. Head of Innovation Group Steve Ellis sees virtual assistants helping in three key areas: information updates, transaction capabilities, and customer insights. “We see a big future here,” he says, “and that’s why we recently formed our new Artificial Intelligence Enterprise Solutions team within the Innovation Group.”

BANKING ON AI TODAY Artificial intelligence already has significant imprint in many areas of banking such as fraud detection, insurance underwriting, customer service, back office operations and wealth management. Because it demonstrably improves efficiencies, reduces costs, generates smarter insights and provides better user experiences, AI will eventually be used industry-wide at an even deeper and broader scale. The only thing to expect are new and surprising use cases as bot and bank become one. Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

Section

5. Marketing & Advertising

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

5. Marketing & Advertising

HOW A.I. CAN SOLVE THE TOP 3 PAIN POINTS IN MARKETING

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3,874. That’s how many companies are featured on Scott Brinker’s behemoth 2016 Marketing Technology Landscape Supergraphic, which drives home the challenge of navigating the marketing industry. “Marketing has the unique challenge of not having a typical stack or process. If you look into any Fortune 500 company, they will have hundreds of products that they are stitching together,” reports Eric Stahl, an SVP of Product Marketing at Salesforce.

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Leading marketing experts agree that the plethora of tools available to marketers and advertisers is both a blessing and a curse. The variety of options enables marketers to keep up with an ever-evolving digital landscape of consumer and customer behavior, but a professional uses between 20-50 different tools to manage all their tasks. The result is more time spent managing software than managing strategy. Every marketing executive we spoke to also emphasized that vendor variation and incompatibility drives the number one challenge of modern marketing: sourcing and centralizing reliable data.

Every marketing executive we spoke to emphasized that vendor variation and incompatibility drives the number one challenge of modern marketing: sourcing and centralizing

reliable data.

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To understand how A.I. can address modern marketing challenges, you first need to understand the tools marketers use every day. If you aren’t a professional marketer, read our brief introduction to marketing technology covering the typical enterprise “stack” and various add-on solutions.

CHALLENGE #1: LACK OF RELIABLE, CENTRALIZED DATA Every function in a corporation struggles with data collection, cleansing, and centralization, but inconsistent standards and non-existent integrations make consistent data flow especially difficult for marketers. Along with tool variation, marketing tech (MarTech) and adverting tech (AdTech) are often siloed from one another. Ritchie Hale, CIO at TouchCR, warns that “the separation of these two stacks creates an identity gap, where we are trying to uniquely identify the person we are advertising to before we have an identifiable piece of information from them.” One key driver of the lack of interoperability between tools is the breakneck speed of technical development and acquisitions in MarTech. “Most DMPs and marketing automation tools possessed by companies like Oracle, Adobe or Facebook were acquired recently and haven’t even been integrated with each other,” explains Monika Ambrozowicz, Global Marketing Manager at Synerise. Another problem, pointed out by Mark Kovscek, President of Velocidi, lies in the nuances of preparing data for different marketing purposes. “As the data is cleansed and prepared, it is optimized for a specific use case (e.g., programmatic, campaign reporting, media attribution) and therefore creates competing versions of the data.” Without reliable, centralized data, marketers are doomed to suffer many inefficiencies and lost opportunities. Many key decisions, such as creative, messaging, and campaign parameters are essential to the success of marketing, yet driven by gut or laziness rather than science. Even with haphazard decisionmaking, marketers are still spending over three hours on average every week just analyzing disparate data sources. Applied Artificial Intelligence

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Another insidious effect of not coordinating between tools is the risk of undermining your own marketing campaigns. Mark Torrance, CTO of RocketFuel, warns “if multiple partners are used to buy traffic based on similar data or targeting ideas, they may bid against each other, driving up the cost (and driving down the efficiency) of the buy.” The effects of poor data centralization also lead to a poor consumer experience. Retailers employing ad retargeting often don’t realize when you’ve already made a purchase and send you tons of irrelevant, useless, and annoying ads. How AI Addresses This Challenge Many MarTech companies aim to be an A.I. layer that centralizes and manages communication and data across marketing tools. The players best positioned to win are major enterprises like Salesforce, Oracle, and Adobe, which already offer end-to-end solutions within their own ecosystems and can aford to aggressively acquire and integrate smaller players. Salesforce CEO Marc Benioff spent over $4 Billion in 2016 buying A.I. companies to roll into Einstein, an A.I. layer that optimizes results across all of Salesforce’s Clouds. One client, Fanatics, is a sports merchandise retailer using Marketing Cloud Einstein to personalize product recommendations. Sports fans come in different flavors, such as hardcore fans, gear fanatics, fans of a school or local team, and fans of fans, i.e. customers purchasing sports gear for friends and family. According to Stahl, an SVP at Salesforce Marketing Cloud, Einstein segmented customers into these large buckets, but also provided “sub-segment targeting” that led to 15-20% of clickthroughs on Fanatic’s email campaigns. The same personalization can be ported to Salesforce Commerce Cloud, where Einstein has helped some customers achieve 28% more revenue and 11% increase in average order value (AOV) from better recommendations.

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Similarly, Adobe controls a vast suite of creative tools as well as a popular data management platform (DMP) and web analytics for enterprises. Vice President Amit Ahuja oversees Sensei, Adobe’s AI layer which unifies data across their cloud solutions. Since Adobe owns the underlying video and content creation tools, Ajuha explains that Sensei is able to “collect rich data across all creatives and all metadata to inform a brand owner which creative to put in front on a consumer.” “When the A.I. hype dies down, the differentiation will be at the data layer,” predicts Ahuja. “Outside of Google, we’re sitting on the biggest system of record for any behavioral digital data. No one else can do what we do.” Behemoths like Salesforce and Adobe are not the only companies tackling the data challenge in marketing. Smaller companies like Swiftype address the common bottleneck of coordinating assets and documents in marketing workflows. Waiting on the creative department to send you copy and graphics while analysts pull the latest campaign performance metrics is painful and inefficient. Swiftype consolidates knowledge and data from multiple sources, like Marketo and Salesforce campaigns, task management tools, and repositories like Dropbox and Google Drive. “Customers in commerce reap the benefits in obvious ways (when a search for ‘spoon’ also serves up search results for items labeled ‘ladle,’ for example),” explains Praveena Khatri, Swiftype’s VP of Marketing, “while publisher customers have more effective indexing of stories and can organize content by relevancy and date.”

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CHALLENGE #2: TALENT BOTTLENECK Mastering a myriad of tools also presents training challenges for marketing teams and creates expertise bottlenecks. Training junior employees to navigate complex enterprise software is tedious and error-prone. “To cut corners, employees often build campaigns around only one or two parameters,” explains Mark Shore, Co-Founder of Strike Social, “which leads to the lowest common denominator and weaker performance.” Another challenge with larger enterprise is dependency on external talent for essential work. Torrance of Rocket Fuel explains that most Fortune 500 companies rely on marketing partners to buy media in the “walled gardens”of Google, YouTube, and Facebook. Additionally, most of them “still have the bulk of their media spending controlled and managed by Ad Agency partners, who have their hands on the keyboards of the platforms.” If mastering marketing tools is difficult, mastering A.I. research and development is even harder. Very few organizations are positioned to succeed on this front. Even if a company miraculously centralized reliable and high-volume data in a single system, specialized talent is still required to design and operationalize working models. “Fortune 500 companies simply do not have the inclination or resources internally and they routinely, if not universally, fail at the BUILD. So the question is how to BUY,” advises Tasso Argyros, CEO of ActionIQ. Even companies with teams of data scientists and engineers on hand often find their employees lack the requisite advanced mathematics background to truly innovate in modern artificial intelligence Engineers alone also do not ensure success. Marketing executives agree that domain expertise and business needs should drive A.I. research, not the other way around. “The most important thing is not advanced math models or complicated neural networks,” Yulia Khansvyarova of SEMrush clarifies. “The most important part is feature engineering. The more domain knowledge you have, the better. Constantly verify your hypothesis with your end users.”

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How AI Addresses This Challenge Automation of tedious marketing tasks improves accuracy and reduces workload, allowing marketing teams to be more efficient and effective. While the landscape of providers offering automated solutions is still crowded, many marketing executives are seeing early traction. Shore of Strike Social claims their technology “automates the tedious process of campaign setup, spots nuanced patterns undetectable to humans, and breaks ad campaigns into several micro-campaigns and shifts ad dollars to the bestperforming targets in real time.” The company was able to improve YouTube view rates by 25% while reducing execution time by 75%. “By reducing execution time,” Shore clarifies, “we mean reducing your staff by up to 4x.” RocketFuel is another A.I. driven marketing company replacing manual optimization with smarter automation. “We do this using a variety of machine learning and optimization techniques, including neural networks, logistic regression, multi-armed bandit, performance-aware pacing, bid multipliers, and more. We frequently balance scores from 2 or more models to achieve multiobjective optimization,” explains Torrance, RocketFuel’s CTO. One agency held a four way competition with RocketFuel and three other vendors, all of whom were doing manual optimization. RocketFuel’s A.I. powered optimization bested the competitors by 8x on cost per acquisition (CPA). Aside from invisible automation layers, innovative marketing firms are also exploring conversational approaches. Equals3 built Lucy, a “cognitive companion for marketers” as described by Managing Partner Scott Litman. Lucy acts as your trusted marketing analyst, helping you with research, segmentation, and planning. Only she works 24/7 and gets smarter with more data. Litman claims that Havas Media, one of the world’s largest media agencies, has successfully used Lucy to achieve a “75% reduction in vendor cost and 7x faster campaign deployment.”

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CHALLENGE #3: INABILITY TO CALCULATE ROI Data is not created equal. Marketers have a hard time turning data into insights, much less calculating the ROI of their decisions. Ben Plomion, CMO of GumGum, highlights the pervasive pain point where “brands spend more than $60BN globally on sports sponsorships but are unable to capture the full value of their logo impressions on broadcast TV and also social media.” Politics may also be interfering with true ROI calculations. Many marketing departments fear accountability and deliberately cherry-pick metrics to present to executives rather than analyze the hard truth of what is really working. How AI Addresses This Challenge New neural network approaches like deep learning have the superhuman ability to detect patterns, leading to many recent breakthroughs in image recognition and computer vision. Computers can not only reliably classify objects in photos and videos, but also identify specific brands and products. Plomion of GumGum explains why such breakthrough technologies revolutionize brand marketing: “Without technology, analyzing a 3 hour game for different logo placements can take days. By leveraging computer vision technology, a machine can simultaneously analyze every sponsor and location within every frame of the video in a matter of seconds, allowing a full game to be analyzed in a few hours or less.” Suddenly, computing ROI on sponsorships or advertising campaigns becomes tractable. Visual intelligence also enables brands to drive engagement through personalized customer experience. Stackla helps brands “discover the best user-generated content (UGC) around their brand, categorize it around customer personas, and recommend the right content for the right marketing channel,” explains CTO Peter Cassidy.

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Using user content found on Stackla’s platform, Virgin Holidays increase bookings by 260% from the previous year and TOPSHOP increased sales of online products by 75%. Cloudsight, another visual intelligence company, helped customers achieve a 4x growth in time-on-site by showing the most relevant images for each user, according to co-founder Brad Folkens. A.I. can also replace older methods to better value different data sources and turn them into more accurate business insights. Superior analysis of audience segmentation and responses lead to improved understanding and handling of customer and lead behavior, such as predicted purchases or churn. TouchCR grew their own business by 20% while reducing ad send by 60% by using A.I. to match marketing efforts to demographic and psychographic indicators. Kazuhiro Takiguchi, CEO of ReFUEL4, produced similar results for clients like Spotify which saw a 40% increase in clickthrough rates (CTR) and 3x app installs over previous campaigns with A.I. powered marketing. According to Takiguchi, such results are achieved by “taking existing and past ad performance to predict future performance of creative.”

CONCLUSION “The holy grail for marketers has always been personalization at scale with affordable customer acquisition costs,” states Pascal Bensoussan, CPO at Reputation.com. To achieve this end goal, “AI and machine learning are becoming table stakes in all the core components of the marketing/advertising technology stack.” Challenges with data capture and centralization, as well as recruiting and training, will continue to plague marketing teams, but the rise of artificial intelligence and machine learning offers a clear way to chip away at these previously insurmountable obstacles in modern marketing.

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6. Beauty & retail

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6. Beauty & retail

CAN ARTIFICIAL INTELLIGENCE GIVE YOU BEAUTY ADVICE?

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The beauty and skincare world is oversaturated, especially if you include all the affordable convenience store brands. If youâ&#x20AC;&#x2122;re a shopper with a budget, you are likely mixing different products and blindly guessing which combinations will work â&#x20AC;&#x201C; like a chemist without a periodic table.

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AI technology has already revolutionized transportation, food, and even health. Why not also beauty and retail? Sephora has launched multiple chatbots, including a bot on Facebook Messenger that lets you book makeover appointments in stores and a bot on Kik that gives product recommendations and beauty tutorials. One of their newest bots, Sephora Virtual Artist, is powered by Modiface, an augmented reality (AR) startup that uses AI to detect faces and project virtual makeup looks in real-time. Olay, a well-known drugstore brand from Proctor & Gamble, created deep learning algorithms to analyze your skin from your selfies and tell you which beauty products to buy. “What we’ve noticed through the last couple of years is that skincare is a very high engagement category and it’s become one of the most confusing and least fun-to-shop categories as well,” said Dr. Frauke Neuser, principal scientist at Procter & Gamble, which owns the Olay brand. “About a third of women walk out of the store without having found that right product for her.” Analysis paralysis from the plethora of drugstore options leads women to resort to department stores where they can have consultations. But these consultations can also feel like overly aggressive ways to sell you products you don’t really want or need. Olay packaged over thirty years of skin analysis and imaging expertise into the Olay Skin Advisor, a mobile experience for empowering consumer choices. 20 years ago, Olay developed a hardware tool for skin analytics called the “Visia Imaging System”. Visia took controlled facial images of users in different lighting conditions and tracked skin conditions such as wrinkles, pores, and textures. Dermatologists took Visia on road shows to analyze how customers’ skin compared to others of their age.

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Such inventions enabled Olay to collect a massive proprietary database of face and skin images from a wide variety of ethnic and demographic backgrounds. Photographic characteristics such as lighting, quality, and shot angles were also diverse. Two different teams – the bioinformatics group and the image analysis experts – collaborated to develop and apply in-house deep learning algorithms to over 50,000 images to determine how skin changes over time and the impact of various products. Eric Gruen, P&G’s Associate Marketing Director, emphasizes that the Olay Skin Advisor is not a downloadable mobile app, but rather a widely accessible web experience that consumers can access through mobile browsers. The AIpowered advisor has been used over 1.2 million times and consistently attracts 5,000 to 7,000 users every day. The Olay Skin Advisor team tested a number of designs for the product to create the best user experience. Psychologically, users feel advice is more trustworthy when personalized. To gather the right information from each user, the Skin Advisor asks a number of questions, such as “What is your age?”, to factor into recommendations. After testing between 4 and 19 questions, the ideal number turned out to be 9. Just like a human beauty consultant, the Skin Advisor also considers a woman’s desired skincare regimen and special requirements. Modiface started with AR for beauty products over a decade ago. 3 years ago, beauty brands noticed that AR was driving sales. Now 80 of the top 100 makeup lines use Modiface, including Sephora, Urban Decay, L’Oreal, and Vichy. CEO Parham Aarabi shares that his company grew over 400% in the last 12 months to meet the incredible demand.

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With Modiface technology, users are able to upload their own photos and virtually try on lipsticks, eye shadows, hair colors, and even dramatic new hair styles. Integration with a new beauty brand typically takes 2-3 months and, once launched, users typically try on 20 or more products per session. These fun, riskfree digital trials lead to an 80% lift in makeup sales when Modiface is added to a brand’s existing website or mobile app. Aarabi, who co-published a paper called Hair Segmentation Using HeuristicallyTrained Neural Networks, plans to use modern AI methods to further personalize the user experience. “Since the appearance of hair can vary based on gender, age, ethnicity, and the surrounding environment, automatic hair segmentation is challenging,” he explains, but emphasizes that deep learning techniques can solve the problem. Additionally, Modiface plans to use AI for “better tracking and detection of faces for improved realism.” Today’s beauty shopper no longer needs to rely on guesswork, Googling, and dumb luck to find the right products for her personal needs. With the promise of AI, even simple decisions such as your drugstore purchases can be guided by thousands of data points and smart algorithms.

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7. Travel

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7. Travel

BALANCING MACHINE LEARNING AND HUMAN INTUITION IN THE TRAVEL INDUSTRY

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Travel planning is incredibly stressful. Between researching options, paying for bookings, and organizing your itinerary, you may also have to contend with the risk of being beaten and dragged off planes.

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Machine intelligence can alleviate some of the pain points for both you and the travel companies you book with. Perhaps no one knows this better than Giorgos Zacharia, CTO of Kayak and holder of a Ph.D. in artificial intelligence and machine learning from MIT. “AI is kind of a fashionable domain at present,” he says, amused by the recent hype, “but we’ve been doing machine learning and AI at Kayak for a long time.” Almost every aspect of your digital user experience is improved with AI. Your preferences towards specific seasons, hotel styles, and price parameters are carefully monitored so that you can be served results you’re likely to book. The photos you see on hotel listings are run through thousands of split tests in which users rank different versions and the results are optimized for mass appeal. Turns out we prefer our hotel photos to be clean and pristine and dislike when they feature other people. Have you ever gone through an entire hotel or flight booking process, only to be told at the end the item was unavailable? Like many industries, travel companies suffer from inconsistency in data. Due to a slew of legacy systems, changes in hotel and airline databases might not fully propagate in time to booking providers to reflect real-time supply. To combat this problem, Kayak’s algorithms analyze a wide variety of historical sources to generate a more accurate forecast of availability. Another common data challenge is handling duplicates. “With all those records coming from different systems, you can have misspellings, different word orders, and other issues that could cause a system to create duplicate records,” explains Zacharia. For example, a single listing could be titled differently as the “Boston Marriott Hotel” or the “Marriott Hotel In Boston.” To save time, the de-duping process is largely automated by machine intelligence. Only lowconfidence records, where the algorithm isn’t sure of a prediction, are escalated to human staff for analysis. Records from different sources may even disagree about basic facts, such as whether a hotel has a pool, but Zacharia assures that “these algorithms can rationalize that data very, very quickly.”

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Machine analysis can yield surprising learnings that contradict your intuition. In a previous role before Kayak, Zacharia built systems to predict corporate bankruptcy filings. One month before a bankruptcy, a company’s credit score often sees a dramatic improvement. The revelation led to further investigation. Turns out CFOs of at-risk companies desperately start paying back overdue bills in the hopes of getting another loan, but typically fail. Similar findings occur in the travel space. For example, users care less about the average review score of a hotel and more about the number of reviews. A hotel with fewer than 24 reviews is far less likely to be booked even if the comments are overwhelmingly positive. Users also have a sophisticated nose for spotting good deals. Broadcasting a clear discount typically results in higher conversions, but even when a hotel discounts rooms without revealing original prices, buyers intuitively flock to the deal. Kayak is hardly the only travel company leveraging machine learning. Booking. com, helmed by CEO Gillian Tans, prides itself on international reach, listing properties in over 225 countries in 43 languages. Many don’t realize that “Booking.com is one of the biggest translation companies in the world,” according to Tans. Headed to a foreign country where you don’t speak the language? No problem. Booking’s cross-platform chatbots allow guests to connect with overseas hotels and hosts and perform real-time translations for all of their supported languages.

Booking travel is not like shopping, or groceries or booking a restaurant. It’s much less frequent, so understanding what works just takes a lot more time. - Gillian Tans, CEO, Booking.com

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In addition to translating human-to-human conversations in real-time, Booking. com bots act as 24-hour customer service agents able to answer most simple travel questions. Kayak also has bots on Facebook Messenger, Amazon Echo, and Slack, in order of popularity. While the natural language processing (NLP) behind the bots are the same, Zacharia notes that users approach different platforms with different intents. “On Alexa, we get more aspirational queries, such as how much a flight is to Hawaii. On Facebook, we get more lower-funnel queries after a user has already booked, such as where they should eat,” he reveals. Complex questions still need to be handled by humans, but Tans remarks “it is surprising how much you can do with machine learning and how good it is getting.” While the improvements brought by machine learning are impressive, the travel industry must overcome many barriers to reach Tans’ ultimate vision of AI being a “fully-functional digital travel assistant that can proactively solve potential issues before you even know they exist.” “Booking travel is not like shopping, or groceries or booking a restaurant. It’s much less frequent, so understanding what works just takes a lot more time.” Tans also emphasizes that we must aim for the correct balance between human interaction and sufficient automation. “Travel is a combination of the personal and the emotional,” she says. “Every customer is different and the travel experience is completely fluid, but the end goal is to find the best solutions.”

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8. Love & dating

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8. Love & dating

BITS OF LOVE: HOW AI FILLS OUR HUMAN NEED FOR INTIMACY

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Even in a highly connected world, you can still be lonely. What’s the point of thousands of contacts on social media if they’ve all friendzoned you? If you’re short on admirers, perhaps you’ll find solace in an algorithm. Following the incessant launch of life-like robots and chatbots which get you laid, experts predict that human-robot marriages will become legal by 2050. We don’t even have to wait that long for technology to fill our intimate needs. Users are already asking Siri to find them a boyfriend. Some are asking Amy — a meetingscheduling virtual assistant — for a date. Applied Artificial Intelligence

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Desperate singles are using the Invisible Girlfriend or Invisible Boyfriend service to create imaginary romantic partners to exchange sweet text messages with. You’ll even get help crafting a believable story behind how you “met”. Even Google’s AI engine is dropping its matter-of-fact style and reading 2,865 romance novels to improve conversational allure and emotional engagement.

Image Credit: Invisible Girlfriend

All these lead to a disturbing conclusion: we may already be living in the dystopian world of Her. Humans are having sex, falling in love, and tying the knot with AI. Even if AI isn’t the object of our affections, technology still plays a critical role in fulfilling our primal need for intimacy. And, it’s only getting better.

ARTIFICIAL INTELLIGENCE BECOMES EMOTIONALLY AWARE New Zealand based startup Soul Machines recently created Nadia, a visually captivating and photorealistic chatbot that conveys and reads human emotions. Nadia sees humans via webcam and determines their emotional state by analyzing facial features in real-time. Nadia is backed by formidable talent. She’s designed by Mark Sagar, winner of numerous Academy Awards for his work on facial motion capture techniques in the films King Kong and Avatar, and voiced by highly acclaimed actress Cate Blanchett.

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While Nadia’s first real-world application is to assist patients with disabilities, you can easily see how her “emotionally charged” service can easily be adapted to solve the human longing for romantic relationships.

SEXBOTS BECOME REALISTIC AND INTELLIGENT The technologies needed to design the perfect sexual partner — attractive, empathetic, human-like, and eager to please — remain fragmented, but trends indicate they are converging to meet people’s true desires. RealDoll plans to integrate artificial intelligence and robotic features into their hyper realistic doll products, with the aim of launching sexbots that rival the realism and sensuality of West World’s hosts by the end of 2017. These sexbots — to be marketed under the trade name Realbotix — will be able to communicate, talk dirty, demand foreplay, and even perform endearing, nonsexual acts. In a 2015 interview with The New York Times, RealDoll CEO Matt McMullen said these sexbots will establish emotional connections with their users. The first release, named Harmony, is expected to debut in April according to London-based Daily Star and will come with an app that allows users to configure her personality. Meanwhile, another AI-driven sexbot named Silicon Samantha has already hit the shelves, with a set of advanced features such as realistic reactions to being kissed and touched, a fully functioning G-spot, and even a kid-friendly “family mode”.

Image Credit: Design Taxi / Silicon Samantha

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CHATBOTS HELP YOU FIND LOVE, DATES, AND SEX. After gaining traction in a wide range of fields such as customer service, back office jobs, health advice, and marketing, chatbots are being deployed to address the fundamental human need for romantic companionship. From rudimentary versions of fembots on Tinder to specialized services like Bubby and Ghostbot, chatbots have become smarter at matching potential partners, preventing sexual harassment, and inspiring romance. Two Google Home devices positioned side by side even found themselves getting attracted to each other. “I love you around the universe to the stars and back,” said one to the other. You can watch the bizarre whole affair unfold on Twitch. Since we last wrote about chatbots for love and connection, several new bots have come to market that are worth a look: • OutChat is a location-based dating bot for Facebook Messenger which enables users to find prospective dates nearby faster and easier. • Bernie is an “AI matchmaker” who understands your “type” and matches you up with the mathematically optimal potential partners. • Sensay is a bot that helps you find other humans who share the same interests. While the irony of going through a bot to talk to a human is not lost on us, beggars can’t be choosers. If you’re feeling a little lonely, there’s no shame getting a little help from your bot friends. • NearGroup is a bot that started out just like Sensay, connecting neighbors with similar interests, but found its calling as a speed dating bot. • Foxsy ironically took the opposite path as NearGroup, starting out as a bot for dating but pivoting to a bot for making friends. Great romances often start out as friendships, so don’t be discouraged from giving this bot a whirl.

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YOU DONâ&#x20AC;&#x2122;T NEED TWO TO TANGO AI-powered bots can be both the object of your affection and a tool that helps you find a soulmate. Either way, theyâ&#x20AC;&#x2122;ve irrevocably influenced the intimate and romantic concerns of us humans. We either use AI to extend our humanity or allow technology to blur the lines of what humanity means. Will love between man and machine ever be socially acceptable? Only time will tell.

From rudimentary versions of fembots on Tinder to specialized services like Bubby and Ghostbot, chatbots have become smarter at matching potential partners, preventing sexual harassment, and inspiring romance.

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9. Healthcare

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9. Healthcare

THE OPPORTUNITIES & CHALLENGES OF A.I. IN HEALTHCARE

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When we asked dozens of venture capitalists where they see the most potential for applied artificial intelligence, they unanimously agreed on healthcare. Technology has already been used to incrementally improve patient medical records, care delivery, diagnostic accuracy, and drug development, but with A.I. we could achieve exponential breakthroughs.

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Deep learning first caught the media’s attention when a team from the lab of Geoffrey Hinton at the University of Toronto won a Merck drug discovery competition despite having no experience with molecular biology and pharmaceutical development. Recently, a multidisciplinary research team at Stanford’s School of Medicine comprised of pathologists, biomedical engineers, geneticists, and computer scientists developed deep learning algorithms that diagnose lung cancer more accurately than human pathologists. The ultimate dream in healthcare is to eradicate disease entirely. This dream might be possible one day with the assistance of AI, but we have a very very long way to go.

INNOVATION IS CHALLENGED BY RISK-AVERSION AND DIGITIZATION “Healthcare as a system advocates ‘do no harm’ first and foremost. Not ‘do good’, but ‘do no harm’. Every application of A.I. in healthcare is regulated by that fundamental philosophy,” cautions Kapila Ratnam, PhD, a scientist turned partner at NewSpring Capital. Additionally, Lisa Suennen, Managing Director at GE Ventures highlights that “the single biggest contribution to excess cost and error in healthcare is inertia.” The attitude of “this is how it’s always been done” is literally killing people. Other investors agree that the ultra conservatism in the healthcare system, while intended to protect patients, also harms them by restricting innovation. Gavin Teo, Partner at B Capital Group and a specialist in digital health, cites “provider conservatism and unwillingness to risk new technology that does not provide immediate fee-for-service (FFS) revenue” as a major challenge for startups tackling healthcare. Teo also points out that the industry feels burned from recent experiences, such as “electronic medical records (EMR) digitization regulations, which were overhyped and resisted.”

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There are many well-known challenges to implementing machine learning and A.I. in healthcare. The first is the lack of “curated data sets,” which are required to train A.I. via surprised learning. “Curated data sets that are robust and have both the breadth and depth for training in a particular application are essential, but frequently hard to access due to privacy concerns, record identification concerns, and HIPAA,” explains Dr. Robert Mittendorff of Norwest Venture Partners. Summerpal Kahlon, MD, is Director of Care Innovation at Oracle Health Sciences. He’s seen many of these data challenges first hand in delivering technological infrastructure to support individualized care. “Adverse drug events cause around 770,000 injuries and deaths annually in the U.S. and cost each hospital up to $5.6 million annually,” Kahlon discloses, “but drug data is messy, coming from multiple sources in multiple formats. Additionally, genetic data in support of pharmacogenomics is not available at scale yet.” Fixing accidental hospital infections and performing rare disease detection with A.I. also requires better data than is currently available. According to Kahlon, the genetic and behavioral data required for rare disease studies are “not welldefined nor easily captured” while “much of the information relating to the risk factors for hospital-acquired infections is kept in unstructured notes in the chart, including in flowsheets and clinical notes.” While data problems in healthcare abound, another major challenge is designing technical solutions that can be smoothly implemented and integrated into clinician practices and patient care. “Behavioral change is the blockbuster drug of digital health,” claims Dr. Mittendorff, but changing habits is much easier said than done. The wrong solution or rollout can even harm the healthcare industry.

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How machine Learning transforms How We Live & Work

9. Healthcare

Implementing and integrating technology has indeed been a burden for many clinicians and practitioners. Dr. Jose I. Almeida is a pioneer in endovascular venous surgery who has practiced for over 20 years. He adopted electronic health records (EHR) ahead of the curve, yet has not seen many of the promised benefits. “We implemented our first EMR System eight years ago hoping it would improve efficiencies. We are now on our fourth system, and remain disappointed,” complains Dr. Almeida. “Right now, it’s been more of a hassle than a time-saver, and has actually disrupted the doctor/patient relationship by forcing a screen between physicians and their patients.” Leonard D’Avolio, founder of Cyft, has harsh feedback for fellow entrepreneurs trying to tackle the space: “We’re seeing hospital after hospital take incredible loss and have widespread layoffs simply from the challenge of implementing electronic health records. Imagine what happens if you then show up and say ‘I have artificial intelligence’.” The healthcare industry is just getting its arms around capturing data digitally, yet many healthcare tech entrepreneurs mistakenly believe that creating a dashboard or dropping in a product will somehow lead to adoption of technology and improve operations. “There’s a huge misconception that A.I. requires huge amounts of data, but that’s not the real issue in healthcare. The real issue is understanding the context into which you are trying to introduce these technology,” warns D’Avolio. “You need context and a deep understanding of who will use this. What workflows will be introduced?” Even if a medical provider does successfully digitize their data, technical carelessness can introduce problems for everyone in the system. According to Ratnam of NewSpring, “A credit card record costs about 10 cents on the black market. A medical record costs about $200. Medical data is so valuable that hackers constantly seek ways to break into provider or payment systems and other repositories of medical data.”

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

9. Healthcare

There is often tension between a venture-backed company, which aims for fast growth, and the healthcare system which challenges scale because of environmental complexity and unavoidable hand-holding. “This lesson has not been widely learned,” observes D’Avolio.

…BUT OPPORTUNITIES ABOUND AND SOLUTIONS EXIST Despite challenges, innovation in healthcare must continue. According to Teo of B Capital, “A study by the Association of American Medical Colleges estimates that by 2025 there will be a shortfall of between 14,900 and 35,600 primary care physicians.” At the same time, the population is aging and in need of more medical attention. Thus, inaction and failure to innovate may lead to doing harm. Luckily, many companies strive to address these issues before they come to pass. CB Insights recently profiled 106 different artificial intelligence startups in healthcare tackling the various challenges in the space, ranging from patient monitoring to hospital operations. Teo identifies A.I. powered chatbots and virtual assistants as one way to “alleviate supply constraints by widening the reach of video telehealth options. In this case, diagnosis can be powered by machine learning and then trained by artificial intelligence.” Examples of companies providing clinician assistant and care delivery services include Babylon Health, Evidation Health, Sensely, and Senior Link. Artificial intelligence can not only improve care delivery, but also assist in clinician decision-making and operational efficiency, amplifying the impact of each individual practitioner. AnalyticsMD employs AI and ML to streamline hospital operations in emergency rooms, operating rooms, and in-patient wards, while predictive companies like Cyft and HealthReveal analyze disparate data sources to accurately triage and apply interventions to the highest risk patients.

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How machine Learning transforms How We Live & Work

9. Healthcare

A.I. not only helps physicians, but also patients. A study by the Mayo Clinic determined that 50 percent of patients have difficulty with medication adherence. Companies like AI Cure employ computer vision techniques to enable smartphones to recognize faces and medications, lowering the cost and improving the effectiveness of tracking and adherence programs. According to Dr. Mittendorff, “AI enabled coaching will allow a provider or coach to manage more than 1,000 patients simultaneously rather than 50-100, a 10x increase in labor leverage.” Finally, drug discovery companies like NuMedii and Kyan Therapeutics de-risk the drug development process, enabling “powerful and proprietary new combination therapies, as well as individualized treatment with unprecedented efficacy and safety,” according to Teo. Otherwise, Suennen points out that the “general spend for each drug brought to market is $2.5 Billion.”

Image Credit: CB Insights

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

9. Healthcare

Even technology challenges that come with digitizations can be mitigated by A.I. Remember how valuable medical records are to hackers? Many of these records are pilfered through social engineering methods, such as phishing or fraudulent phone calls. Protenus is a healthcare security company which applies A.I. to analyze enterprise-wide access logs and flag suspicious cases for administrator review.

ALIGNING WITH POLICY & REVENUE IS KEY TO SUCCESS The key to adoption of healthcare IT is to identify the correct point of entry and fit these systems seamlessly into existing workflows. D’Avolio of Cyft has spent over 12 years fitting machine learning into the healthcare system, yet when he speaks at conferences for clinicians, he avoids using the words “artificial intelligence” or “machine learning” and instead focuses on real impact and benefits. Many patients with chronic diseases like diabetes visit doctors and hospitals numerous times, costing themselves, insurance providers, and the medical system a substantial amount of money. Cyft builds sophisticated models that identify patients with a preventable re-admission and matches them to appropriate intervention programs. Traditionally, these decisions are made by looking at 7-10 administrative variables, but Cyft’s models looks at over 400 data sources, ranging from free-text input from nurses to call center data. While adoption of such technologies may seem complicated, D’Avolio gets buy-in by strategically aligning with revenue incentives and policy decisions. “In healthcare, policy eats strategy and culture for breakfast,” explains D’Avolio. “For example, prior to the American Recovery and Reinvestment Act passed in 2009 the rate of adoption of electronic health records was under 9%. Today, thanks to the carrot and stick incentives involved in that act the rate of adoption is > 90%.” Another major policy shift that has dramatically helped investment in healthcare IT are the value-based care experiments (also called demonstration programs) funded by the Center for Medicare & Medicare Innovation (CMMI).

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

9. Healthcare

Knowing which policy an organization is incentivized or paid by is key to identifying promising customers. According to D’Avolio, “organizations that get paid mostly from seeing more patients will want AI that helps deliver more complex care faster. Organizations that are paid via value-based programs will seek technology that keep patients healthier at lower cost.” Suennen of GE Ventures agrees that operational analytics can dramatically improve health systems. “25 percent of the more than $7 billion spent each year on knee and hip surgeries are impacted by bundled payments initiatives. Determining how to manage these bundles is challenging, and advanced technologies can aid in understanding what changes must be made across the board in operations and financial/clinical management to ensure that health systems can respond.” Teo is also excited by policy changes that should drive forward healthcare innovation. “New reimbursement driven by the Medicare Access and CHIP Reauthorization Act (MACRA) and the Merit-based Incentive Payment System (MIPS) incentives in 2017 will drive quality outcomes, phasing providers to think more holistically when investing in technology.” Additionally, he believes that a looser FDA in the coming years will help drive investment in personalized medicine. Successful healthcare innovation will only happen with strong collaboration between entrepreneurs, investors, healthcare providers, patients and policy developers. If the stars align, humanity stands to derive enormous benefit from the application of A.I. and inch closer to our dream of perfect health and a world without disease.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

Section

10. Gaming

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

10. Gaming

THE POINT OF PLAY: HOW GAMES MAKE AI SMARTER

Image Credit: Shutterstock / Dragon Images

Do video games make you dumb? You can probably think of someone in your life whoâ&#x20AC;&#x2122;d make you nod vigorously, but according to behavioral psychologists, playing games actually does the opposite. Play performs a crucial role in cognitive development and building behavior that helps advanced animals thrive in different situations and environments.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

10. Gaming

Turns out what helps kittens and toddlers learn can also help AI get smarter. In March 2016, Google’s Alpha Go thrashed 18-time human world champion Lee Sedol, in a five-game match of Go. Alpha Go’s neural networks played tens of millions of matches against both humans and itself to master the complex and unusual gameplay it used to pummel Lee. The achievement reveals how machine learning works: an AI system progressively makes self-adjustments to adapt to new challenges and become unbeatable. Practice makes perfect, in gaming as in life. Algorithms bested the top humans at Tic Tac Toe in the 1950s, checkers in 1994, chess in 1997, Jeopardy in 2011, and finally Go in 2016. Right up to the victories, many considered these games to be impossible for computers to master. Just several months after scoring the surprise Go win, Deepmind – the company behind Alpha Go – announced a partnership with game developer Blizzard Entertainment to open their 20-year game series Starcraft to AI and machine learning researchers around the world. Among the finest player-versus-player (PVP) games in history, Starcraft – with its variable gameplay approaches, wide range of playable units, and rich in-game economic system – serves as a worthy learning environment to teach AI how to make strategic decisions and solve complex, real-world challenges. Chess and Go are games of perfect information, in which both sides are fully aware of the entire game state. In Starcraft, you don’t know where your enemy is or what she is building. The game uses a fog-of-war layer to obscure information from players — both AI and human. This layer necessitates hierarchical planning in addition to memory and responsive adaptation to new data in order to win a match. In turn, these parameters are perfect for probing the limits of reinforcement learning.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

10. Gaming

Image Credit: Blizzard Entertainment

In Starcraft, you never know when an enemy might leap from the shadows.

BEYOND SCRIPTED NPCS AND BOTS Gone are the days when gaming AI simply meant the scripted non-player characters (NPCs) you meet in role-playing games (RPGs) or the crazy bots you get to shoot when no one else is around to play against. While a rudimentary form of programmatic scripting made up the entirety of in-game “AI” in the past, much has changed. For example, the multi-awarded and critically acclaimed Shadows of Mordor – a role playing game based in J.R.R. Tolkien’s world of Middle Earth – introduced the AI-driven Nemesis System in 2014 with a reported upgrade coming in mid-2017. The machine learning component adds a realistic layer of emotion, character and personality development to in-game entities such as orcs, depending on how their series of encounters with the human player pan out. Gaming companies like Electronic Arts (EA) and Unity, indie developers, and even robotics startups are using character AI to improve the realism of their experiences. Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

10. Gaming

In an interview with TOPBOTS, Patrick Soderlund, SVP at EA Worldwide Studios emphasized his company’s heavy investments in AI. He said, “AI is the strongest driver of technical innovation in the gaming industry and in the larger field of software development.” Soderlund, who oversees many of the gaming behemoth’s popular releases such as Battlefield and Need For Speed, described an ideal scenario where AI-powered characters in games make independent decisions and anticipate what a human player will do based on her playing patterns. To Soderlund, this requires AI to not only be smart, but also emotionally intelligent. “In most games, the players can always find a way to cheat AI or figure out a way to work around the AI. But with a self-learning and adaptive AI that actually understands what you are doing, then you’ll have a real artificial intelligence, not one that is merely scripted to deal with certain events.”

What people tend to forget is the actual player experience. Experience consists of so many things: it’s animation, it’s graphics, etc. There’s a lot artificial intelligence can do but not until all of those things act well together as an entity will the experience to the player become relevant. The most important thing is not the underlying AI system but the consumer-touching experience. - Patrick Soderlund, Senior Vice President, EA Worldwide Studios

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

10. Gaming

The use of AI in games has also gone beyond virtual opponents, NPCs and creeps (the jargon game designers use to describe programmatically spawned monsters). Soderlund reveals that AI can streamline realistic content creation, personalize experiences, and make games run faster and easier to test. “You’ll be able to talk to a video game who’ll act smartly and come back to you with context, sensitivity, and emotion. That will be pretty cool in terms of storytelling,” he promises. With AI currently getting a tremendous level of attention, Soderlund calls for caution, especially among over-enthusiastic game designers who think AI will solve everything. “What people tend to forget is the actual player experience. Experience consists of so many things: it’s animation, it’s graphics, etc. There’s a lot artificial intelligence can do but not until all of those things act well together as an entity will the experience to the player become relevant. The most important thing is not the underlying AI system but the consumer-touching experience.”

MASTERING THE REAL GAME OF LIFE In a move similar to the Deepmind-Blizzard collaboration, Microsoft opened up Minecraft for AI research. Dubbed Project Malmo and open to all AI designers on GitHub, the initiative seeks to use a robust game environment in creating “systems that can augment human intelligence” in the real world, able to perform various tasks from cooking and driving a car to doing the laundry and performing medical surgery. Co-founded by business magnate and tech visionary Elon Musk, OpenAI joined the gaming fray, widening the net and diversifying the experiences AI researchers can immerse their agents in. The San Francisco-based AI lab launched a collection of virtual worlds called Universe where AI can learn to play games and use common software applications. The underlying goal is to develop machines with human-like “general intelligence,” able to adapt, make decisions, and take actions in a wide range of environments and scenarios.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

10. Gaming

Allowing AI to experiment and fail in virtual environments may be much safer than letting them loose in the world. The extraordinary realism that goes into modern games is useful not only for appeasing picky gamers, but also for training AI for use cases such as autonomous vehicles. A team from Intel and TU Darmstadt claim the visual fidelity of popular games like Grand Theft Auto can be used to establish “ground truth” – or a baseline for reality – for tuning vision algorithms for self-driving cars. Let’s just hope AI doesn’t also pick up the less civilized aspects of playing GTA. We’ve come a long way since Deep Blue defeated world chess champion Gary Kasparov in 1997. IBM Interactive Media CTO George Dolbier aptly describes the gigantic leap: “the number of potential moves in a chess game has been equated to the number of atoms, or the number of grains of sand on beaches. The number of moves that are potential on a game of Go are equal to the number of atoms in the universe.” In a 2014, WIRED reported Go as “one of AI’s greatest unsolved riddles” that would require another 10 years to crack. Given the unexpected advances of Go-playing algorithms, you can imagine how AI with deep learning capabilities – immersed in even more challenging environments like Starcraft – will eventually crack the game and beat any human opponent. But the stakes for emerging technology are much higher than merely winning or losing games. If artificial general intelligence (AGI) can be achieved by pitting algorithms against a multitude of virtual words, then what you have is a gamechanging revolution.

Applied Artificial Intelligence

How machine Learning transforms How We Live & Work

CRAVING MORE CONTENT? HERE’S HOW YOU CAN WORK WITH US TOPBOTS is a strategy & research firm focused on applied AI for enterprises. Our customers include leading global companies such as L’Oreal, Paypal, and WPP. We advise business leaders, executives, and practitioners on emerging technology trends and help you successfully apply them in your organization. • To discuss how you can adopt automation technologies and AI at your own organization, contact us at strategy@topbots.com. • To get your executive team up to speed on emerging technologies and their impact on your industry, ask us about our corporate education programs by emailing education@topbots.com. • To raise your A.I. IQ, read our publication at TOPBOTS.com or subscribe to our newsletter. • To order the full version of our Applied Artificial Intelligence book, visit appliedAIbook.com. Copyright 2017 TOPBOTS INC