with impaired vision, for use in sterile clinical environments where physical interfaces aren't appropriate, and for the 80% of people who either work with their hands or in highly mobile environments.
Artificial intelligence—in the form of speech-to-text, NLU, NLG, and speech synthesis—underpins the operation of voice platforms. Continued advances in AI will make conversational computing sound more human. Future voice agents will engage in complex back-and-forth conversations, speak realistically, use human idioms, and even add fake breathing sounds and natural hesitations and pauses to make them sound more human. Google's Duplex technology and Microsoft Cortana have already made great strides and we should expect major breakthroughs in this area in the coming years.
As voice agents become more sophisticated, they will become ever-present in our lives and help us navigate our days. We will use them to make reservations, manage our calendars, run errands, place orders, troubleshoot problems, give advice, and even provide emotional support. Ultimately, a transactional conversation with a digital voice agent will become indistinguishable from one held with a human. This prospect has profound implications for those that work in customer service.
Exploration and Discovery
The New York Police Department (NYPD) uses PATTERNIZR, an AI-based discovery tool, to spot crime patterns. With more than 68,000 robberies, larcenies, and burglaries occurring in New York in 2018 alone, the NYPD will take all the help they can get. The NYPD is split into 77 separate precincts. PATTERNIZR, rolled out in December 2016 but only revealed to the public in early 2019, looks for crime patterns that span precincts. PATTERNIZR frees up human analysts to focus on more complex analysis tasks. Just like a human analyst, PATTERNIZR compares factors including method of entry, the type of items stolen, the distance between crimes, and so on. To eliminate racial bias, the system is not given the race of suspects. PATTERNIZR has already proven useful. For example, the AI found a link between crimes that spanned precincts that had not previously been flagged as connected. In two cases, a man used the threat of a syringe to steal a drill. The AI identified two other instances where a syringe was used as a threat in robberies. The NYPD used the information to locate the suspect and arrest him. He pleaded guilty to larceny and assault.
AI's predictive capabilities are a powerful tool for researchers. Material scientists use AI to predict the structures of materials that may have a desired set of physical properties. New alloys and compounds may be discovered as a result. AI-guided research could lead to the discovery and synthesis of new wonder materials such as room-temperature superconductors and high-efficiency battery electrolytes that would transform the energy sector and help to address the climate challenge. As we review in Chapter 10, pharmaceutical companies use a similar approach to help them discover new drugs. Predictive AI may help us discover therapeutic drugs that transform human health. How might AI boost your company's research efforts?
Better-Informed Decision-Making
Business analytics refine data to offer all manner of insights. Some use statistical techniques, some use heuristics, and many now leverage the power of AI to find associations in data, extract insights, and make recommendations. Intelligent decision support systems use such analytics to support data-driven decision-making. Credit agencies like Experian and credit card companies including American Express use machine learning to boost the speed and accuracy of credit approvals by crunching terabytes of consumer data. Some Customer Relationship Management (CRM) platforms use AI to intelligently prioritize leads. AI is used to guide recruitment decisions, spending decisions, investment decisions, purchase decisions, marketing decisions, design decisions, engineering decisions, and much more.
Mortgage companies use AI to assess loan risk and guide underwriting decisions. Underwriters make the near-Shakespearean decision of “to loan, or not to loan” with risk models built on historical data. An underwriter assesses risk using 10 to 15 data points about a prospective borrower: salary, credit score, debt-to-earnings ratio, and so on. Based on this assessment, the underwriter either gives the thumbs up or thumbs down. Limited data doesn't provide a full picture of a person's ability to repay a loan. There's more to a person than 10 to 15 data points. Underwriters use this narrow data set to limit complexity and manage their workload. An underwriting AI considers hundreds of data points about a person and finds complex associations that create a more nuanced picture of a prospective borrower. Zest Finance and Underwriter.ai claim their underwriting AIs find low-risk loan candidates who don't qualify with a traditional underwriting approach. The upside for mortgage companies: they sell more loans without increasing their risk.
Predicting the Future
AI offers us a crystal ball. When AI is applied to historical data, it finds patterns and complex associations that allow it to make high-quality predictions about what might happen next. AIs are used to predict disease outbreaks, assess actuarial risk, and predict future demand on the electric grid. Atidot, Quantemplate, and Analyze Re use AI to predict insurance risk.
Law enforcement uses AI to predict crime. PredPol is a collaboration between the Los Angeles Police Department (LAPD) and the University of California at Los Angeles (UCLA). PredPol predicts where and when serious crimes are most likely to happen. PredPol scientists claim the system has double the accuracy of human analysts. Importantly, the system only predicts the locations of future crimes, not the identities of people predicted to commit those crimes. We are still a long way from the precrime concept described in Philip K. Dick's Minority Report.
Most businesses need to make forecasts or predictions, and AI is just starting to scratch the surface in this space. AIs will power all manner of business planning systems: demand forecasts, risk analysis, design trends, and more.
Seeing the World through a New Lens with Super Sensors
Sensors, turbocharged by AI to create “super sensors,” will lift the veil from the world, extend our existing five senses, and allow us to perceive the world more fully. Like the microscope before it, AI gives us a new lens to view the world through; a lens to experience the world in all its complexity and beauty. Super sensors will oversee business operations, monitor the operation of equipment, create new products, and provide us a more holistic view of people.
An exciting early example of super-sensing capabilities comes from the work of Dr. Dina Katabi, a professor and research lead at MIT's Computer Science and Artificial Intelligence Lab (CSAIL). Katabi's team has created a super sensor. What adds to the delight of this particular story is that it begins with Star Wars and ends with Star Trek.
As a child, Katabi was fascinated by the notion of “The Force” in the Star Wars movies. Her fascination persisted into adulthood. In Star Wars, Obi Wan Kenobi describes a mystical force that “surrounds us, penetrates us, and binds the galaxy together.” As she considered the fictional notion of The Force, Katabi realized that a real force surrounds us all—electromagnetic energy. If you flap your arms up and down, you literally create a disturbance in that force. Katabi wondered if sensing electromagnetic energy would let her see the world in a new way.
Katabi's research team built a simple wall-mounted sensor and fitted it inside a room. The sensor emits and receives radio frequency (RF) signals, much like a Wi-Fi hotspot. RF signals tend to pass through walls but bounce back from people. The sensor picks up reflected RF signals and feeds data into a neural network that makes sense of the reflected signal data.
To train the AI, Dr. Katabi's team captured video of people moving around inside the room. The AI was fed the video and RF sensor data as parallel inputs. The AI found complex associations between the RF sensor data and the video images and eventually correlated the two. With this insight, the AI can determine what is happening inside
