AI and machine learning changes the software paradigm computers have been based on for many decades. In the traditional computing domain, providing an input, we feed it into an algorithm to produce the desired output. This is the rule-based frameworkthe majority of the systems around us still work with. We set up our thermostat to a desire temperature (input) and a rule based programming (algorithm) will take care of reading a sensor and activating heating or AC machines to get to the room temperature we want (output). The industry has been working relentlessly for many years developing better hardware, software and apps to solve a gazillion problems and use cases around us with programmable solutions.

The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization's business needs requires a deep understanding of their capabilities. If popular culture is an accurate gauge of what's on the public's mind, it seems everyone has suddenly awakened to the threat of smart machines. Several recent films have featured robots with scary abilities to outthink and manipulate humans. In the economics literature, too, there has been a surge of concern about the potential for soaring unemployment as software becomes increasingly capable of decision making. Yet managers we talk to don't expect to see machines displacing knowledge workers anytime soon -- they expect computing technology to augment rather than replace the work of humans.

Westbrook added six rebounds and five assists for the Thunder, who surged in the final minutes of a tight meeting between Western Conference contenders. Although he wasn't near a triple-double for the first time this season, the ferocious point guard carried the Thunder down the stretch, scoring their final six points and making a key hustle play in the last minute.

But, this story is real. About 15 years ago, in Walmart, a sales guy made efforts to boost sales in his store.

Machine learning is a branch of AI. Other tools for reaching AI include rule-based engines, evolutionary algorithms, and Bayesian statistics. While many early AI programs, like IBM's Deep Blue, which defeated Garry Kasparov in chess in 1997, were rule-based and dependent on human programming, machine learning is a tool through which computers have the ability to teach themselves, and set their own rules. In 2016, Google's DeepMind, beat the world champion in Go by using machine learning--training itself on a large data set of expert moves. In supervised learning, the "trainer" will present the computer with certain rules that connect an input (an object's feature, like "smooth," for example) with an output (the object itself, like a marble). In unsupervised learning, the computer is given inputs and is left alone to discover patterns. In reinforcement learning, a computer system receives input continuously (in the case of a driverless car receiving input about the road, for example) and constantly is improving. A massive amount of data is required to train algorithms for machine learning. First, the "training data" must be labeled (for instance: a GPS location attached to a photo).

For consumer companies, large-scale leveraging of customer and behavioral data to drive personalized customer experiences is turning into a virtual arms race. Marketing technology platforms of the last 10 years were built around campaign process that were still highly manual, requiring marketing execs to do all the testing, optimization and which makes the cycle time for learning and actually influencing marketing very slow. Now more and more marketers recognize the need to deploy advanced personalization capabilities that make the use of machine-learned optimization. And, Matt Fleckenstein, Chief Product Officer at Amplero, helps marketers achieve just that. With a track record for conceiving, building, and launching martech products and services it comes easy to him.

Algorithm

Anesthesiologists' jobs look safer than radiologists' jobs. In H. G. Wells's classic The War of the Worlds, the narrator pauses a moment to rue the fact that he didn't react sooner to the arrival of an "intelligence greater than man's"--in his case, Martians landing on earth. Comparing himself to a comfortable dodo in its nest, he imagined those ill-fated birds also dithering as hungry sailors invaded their island: "We will peck them to death tomorrow, my dear." As intelligent technologies take over more and more of the decision-making territory once occupied by humans, are you taking any action? Are you sufficiently aware of the signs that you should?

The use of robotics and artificial intelligence in businesses is on the rise, but there are still significant challenges for organisations adopting the technologies. Two executives from global IT consulting and outsourcing group Capgemini spoke to IoT Hub about how best to meet these challenges and why the returns make the effort worthwhile. "The amount of data that's available now in places like social media and enterprises means it is becoming for efficient for machines to make decisions rather than humans, taking the human bias out of it and making decisions objectively," said Saugata Ghosh, senior manager of digital services at Capgemini. This trend, together with the maturity of robotic process automation (RPA) technologies over the last three to five years, has contributed to the growth in adoption of robotics and AI, Ghosh said. "If you look at the spectrum of robotic automation, at one end you have simple rules-based automation where the economics of those are such that they are quite easy to implement and have strong returns on investment," he explained.

A formal branch of artificial intelligence, machine-learning builds systems that learn directly from the data they are fed and effectively program themselves to analyse that data and make accurate predictions. Having already helped multiple business sectors create new models and drive competitive advantage, now it's the turn of the insurance industry. So just how is machine-learning changing the way insurers do business? "It gives insurers three distinct advantages," explains Max Richter, managing director in Accenture's UK insurance analytics group. "The first is to mine greater volumes of data, the second to scale analytics across the organisation by working smarter and faster, and lastly by answering more complex questions from'will this customer leave me at renewal?' to'what can I do about it?'" As such it is quickly becoming an essential tool for the insurance sector, specifically enabling companies to yield higher predictive accuracy as it can fit more flexible and complex models.