Raghav serves as Content Lead at Emerj, covering our major industry areas and conducting research. Raghav has a personal interest in robotics, and previously worked for research firms like Frost & Sullivan and Infiniti Research. Insurers are looking to leverage all of the digital customer data that is now available to them, including one new data source that some of the largest insurance enterprises claim are actively collecting: real-time data streams from the Internet of Things (IoT). IoT devices, such as in-car sensors, smartphones, and smart appliances, can send insurers data on product usage and driving habits among other behaviors. In turn, this data could be fed into AI algorithms that may allow insurers to offer risk-based pricing and other popular services.

If you take an introductory statistics course, you'll learn that a datapoint can be used to generate inspiration or to test a theory, but never both. Humans are a bit too good at finding patterns in everything. Real patterns, fake patterns, you name it. We're the sort of creatures that find Elvis's face in a potato chip. If you're tempted to equate patterns with insights, remember that there are three kinds of data patterns: Which ones are useful to you?

Every rule based system contains four basic components. Firstly, the system contains a set of rules, also known as the rule base, and acts as the domain of knowledge for the computer. Second, there is an interference engine, also called the semantic reasoner. This component is responsible for interpretation of the rules and taking action accordingly. The interference engine works in three steps: match, conflict-resolution, and act.

When you're creating a chatbot, your goal should be to make one that it requires minimal or no human interference. This can be achieved by two methods. With the first method, the customer service team receives suggestions from AI to improve customer service methods. The second method involves a deep learning chatbot, which handles all of the conversations itself and removes the need for a customer service team. Such is the power of chatbots that the number of chatbots on Facebook Messenger increased from 100K to 300K within just 1 year.

We can describe a chatbot as a computer program that conducts a conversation in natural language via auditory or textual methods, understands the intent of the user, and sends a response based on the business rules and data of the organization. Another way to describe chatbot programming is the concept of "micro-engagement," or technology designed to communicate with customers and prospects at various intervals and via multiple channels in order to drive business interactions. Whatever the digital classification, it's important for boards of directors and C-level executives within the insurance industry to understand that chatbots are an increasingly effective way to improve business processes -- but are not a panacea. Roughly 65% of customer interaction can now be automated, and in order to maximize their effectiveness, chatbots must be wed to a comprehensive communications process that also includes humans (who can step in at the appropriate time). Being able to extract information from an insurance claim is a fairly complex task that demands a human component.

COMPUTER BRAINS are tiny rectangles, becoming tinier with each new generation. Or so it used to be. These days Andrew Feldman, the boss of Cerebras, a startup, pulls a block of Plexiglas out of his backpack. Baked into it is a microprocessor the size of letter paper. "It's the world's biggest," he says proudly, rattling off its technical specs: 400,000 cores (sub-brains), 18 gigabytes of memory and 1.2trn transistors.

Voice assistants like Alexa convert written words into speech using text-to-speech systems, the most capable of which tap AI to verbalize from scratch rather than stringing together prerecorded snippets of sounds. Neural text-to-speech systems, or NTTS, tend to produce more natural-sounding speech than conventional models, but arguably their real value lies in their adaptability, as they're able to mimic the prosody of a recording, or its shifts in tempo, pitch, and volume. In a paper ("Fine-Grained Robust Prosody Transfer for Single-Speaker Neural Text-to-Speech") presented at this year's Interspeech conference in Graz, Austria, Amazon scientists investigated prosody transfer with a system that enabled them to choose voices in recordings while preserving the original inflections. They say it significantly improved on past attempts, which generally haven't adapted well to input voices they haven't encountered before. To this end, the team's system leveraged prosodic features that are easier to normalize than the raw spectrograms (representations of changes in signal frequency over time) typically ingested by neural text-to-speech networks.

Tesla showed the computer at the Hot Chips conference. Designing your own chips is hard. But Tesla, one of the most aggressive developers of autonomous vehicle technology, thinks it's worth it. The company shared details Tuesday about how it fine-tuned the design of its AI chips so two of them are smart enough to power its cars' upcoming "full self-driving" abilities. Tesla Chief Executive Elon Musk and his colleagues revealed the company's third-generation computing hardware in April.

Why Partnership Strategy, not Technology, drives Digital Transformation? Known from the 17th century (Blaise Pascal invoked it in his famous wager, which is contained in his Pensées, published in 1670), the idea of expected value is that, when faced with a number of actions, each of which could give rise to more than one possible outcome with different probabilities, the rational procedure is to identify all possible outcomes, determine their values (positive or negative) and the probabilities that will result from each course of action, and multiply the two to give an "expected value", or the average expectation for an outcome; the action to be chosen should be the one that gives rise to the highest total expected value. Decision theory (or the theory of choice) is closely related to the field of game theory and is an interdisciplinary topic, studied by economists, statisticians, psychologists, biologists, political and other social scientists, philosophers, and computer scientists. The need for decision under uncertainty has never been stronger. Although the digital realm is evolving fast, the partnership strategical choice remains a human prerogative and a key driver of the digital ecosystem evolution.