Sales teams use many systems and applications to run their operations. There are CRM, SFA, order management and billing applications to help capture customer and account information and manage various customer processes. These applications help sales to manage their day-to-day tasks, but are these tools helping them sell more? Why is it that even today, sales teams feel they don't have enough actionable, timely and contextual information to offer the right solution to a prospect and close the deal? Why is it that selling remains more of an art form than a repeatable scientific method? Of course, people play a large role there.

It has never been easier to build AI or machine learning-based systems than it is today. The ubiquity of cutting edge open-source tools such as TensorFlow, Torch, and Spark, coupled with the availability of massive amounts of computation power through AWS, Google Cloud, or other cloud providers, means that you can train cutting-edge models from your laptop over an afternoon coffee. Though not at the forefront of the AI hype train, the unsung hero of the AI revolution is data -- lots and lots of labeled and annotated data, curated with the elbow grease of great research groups and companies who recognize that the democratization of data is a necessary step towards accelerating AI. However, most products involving machine learning or AI rely heavily on proprietary datasets that are often not released, as this provides implicit defensibility. With that said, it can be hard to piece through what public datasets are useful to look at, which are viable for a proof of concept, and what datasets can be useful as a potential product or feature validation step before you collect your own proprietary data. It's important to remember that good performance on data set doesn't guarantee a machine learning system will perform well in real product scenarios.

A*STAR researchers have created a program that predicts public transport usage based on land-use and the location of amenities, an essential capability for smart city planning. From schools and shops to hospitals and hotels, a modern city is made of many different parts. Urban planners must take account of where these services are located when designing efficient transit networks. A*STAR researchers have developed a machine-learning program to accurately recreate and predict public transport use, or'ridership', based on the distribution of land-use and amenities in Singapore1. Traditional cities comprise an inner central business district (CBD), where most people work, surrounded by outer residential and industrial zones.

Over the next five years, Ford will pour $1 billion into an artificial-intelligence company tasked with developing the technology that one day will drive its autonomous vehicles. The technology also could be licensed to other automakers, executives said. Pittsburgh-based Argo AI was founded late last year by Bryan Salesky and Peter Rander, who previously worked on self-driving-car initiatives at Google and Uber, respectively. The company will include staff members at Ford who have been developing its virtual driver system for the past several years. In a phone call Friday, chief executive Mark Fields said the investment will help Ford bring its self-driving cars to market by the company's previously stated goal of 2021.

The bot space has been changing rapidly. A few years ago, bots were automated chat agents. They would aim at replacing customer service representatives in case of traffic spikes or when all were busy. Very rudimentary, they usually left consumers frustrated. Bots are now enjoying an incredible momentum.

Students code software at desktops, while others assemble odd machines with wires and multi-colored boxes. Earning a spot at this elite university isn't easy; UC-Berkeley accepted a mere 14.8 percent of applicants for the class of 2020. So this young crew will likely be tomorrow's tech leaders and pioneers. Despite all the promise, it appears that BRETT is struggling. BRETT is a robot, and he – or she, or it – is attempting to place a small wooden block into a small hole. Again and again, BRETT swings his arm over the opening, attempts to place the block, but fumbles. Just can't make it fit. However, as robots go, BRETT has a huge advantage: he can learn. Every time BRETT swings his arm and fails, he calculates what went wrong. In essence he's doing what we humans do: he's failing, and in response he's deciding how to improve the next effort. I stand watching for about 15 minutes, and finally BRETT succeeds – a lengthy period given the simple task. But the astounding point is that the robot really did learn.

Machine learning, artificial intelligence, deep learning… Unless you've been living under a rock, chances are you've heard these terms before. Indeed, they seem to have become a must for market researchers. Unfortunately, so many precise terms have never meant so little! For computer scientists these terms entail highly technical algorithms and mathematical frameworks; to the layman they are synonyms; but as far as most of us should be concerned, increasingly, they are meaningless. My engineers would severely chastise me if I used these words incorrectly--an easy mistake to make since there is technically no correct or incorrect way to use these terms, only strict and less strict definitions.

For a fleeting moment, the humans thought they had a chance. Four professional poker players were convinced they found a flaw in the sophisticated artificial intelligence software that was beating them in a tournament of no-limit Texas Hold'em. If they bet in odd sizes, it seemed to trip up the computer. Within a day or two, though, that weakness vanished. "It became very demoralizing showing up every day and losing this hard," said Jason Les, who has played professional poker for a decade. When the 20-day tournament was done, the artificial intelligence, called Libratus, won a princely $1,766,250.

Risk prediction platform predicts population health costs and prescribes patient care optimization. According to a Frost & Sullivan report, strong opportunities exist for Big Data in healthcare via population health management, clinical decision support, and the use of real-world data. In fact, solutions that directly impact care delivery and outcomes will be the focus over the next five years. Julie Skeen, Healthcare IT Strategist at Infogix, told Health IT Outcomes, "Big Data may not by itself be able to cure cancer or AIDS, but by applying analytics to human DNA and the DNA of major diseases is already producing positive results for patients. By looking at Big Data, medical researchers can help patients get the best treatment for the type of disease they have, minimize the negative impact of those treatments and in the end save lives."

According to analyst Forrester, artificial intelligence (AI), big data and analytics will increase businesses' access to data, broaden the types of data that can be analysed, and raise the level of sophistication of the resulting insight. For 2017, Forrester expects investment in AI to triple. Read about the new best practices for the ERP systems and how to tackle the growth of ERP integrations. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.