The retail space is changing drastically through the evolution of technology, such as artificial intelligence (AI), which can offer retailers better control over stock flows and better customer service. This technology, and in particular machine learning, can step in to help retailers cater to the needs of their customers in smarter ways. Retail goods, even the most niche products, are becoming more and more commoditised and price parity means retailers have to look continuously for more innovative ways to keep customers loyal. A high-end jewellery retailer, a sporting goods store and a grocer all have very different target markets and their customer's behaviour patterns are very different, but the beauty of machine learning, an emerging technology, is that it quickly adapts to any type of environment. Machine learning is a form of AI that allows computers to learn without being programmed.

DeepMind is now capable of teaching itself based on information it already possesses. In a significant step forward for artificial intelligence, Alphabet's hybrid system -- called a Differential Neural Computer (DNC) -- uses the existing data storage capacity of conventional computers while pairing it with smart AI and a neural net capable of quickly parsing it. "These models can learn from examples like neural networks, but they can also store complex data like computers," wrote DeepMind researchers Alexander Graves and Greg Wayne. Much like the brain, the neural network uses an interconnected series of nodes to stimulate specific centers needed to complete a task. In this case, the AI is optimizing the nodes to find the quickest solution to deliver the desired outcome.

We've created software that combines data from the car with AI / Machine Learning technology and uses those data signals to help auto dealers reduce overhead cost, make better decisions and create contextual messaging to better service their customers.…

You are parent and wish to teach your 8 year old boy how to play violin? But does this have anything to do with artificial intelligence (AI)? Recent scientific experiments have shown that very young babies -- as young as 9-month old -- that learn music can significantly improve many of their cognitive functions, such as their future language acquisition. Children who learn how to play music young get both better verbal and language learning skills than the ones who don't, because they gain enhanced sound representation abilities and modify their brain connectivity. For adults, learning language is a great example.

In a setting befitting the opening scene of a sci-fi thriller at the recently opened Leverhulme Center for the Future of Intelligence at Cambridge University, Professor Stephen Hawking cautions that the future of artificial intelligence could potentially be "either the best, or worst thing to ever happen to humanity." Reiterating his 2014 statement to the BBC, Hawking urges that if done wrong, "the development of full AI could spell the end of the human race" –– think Terminator, I, Robot, or WestWorld. But on the other hand, Hawking believes that amplifying our minds through utilization of artificial intelligence can transform every aspect of our lives. The overarching concern surrounding AI is machine morality and whether it is safe for society. If people do not have proper ethical guidelines or fully comprehend the risks AI could play on mankind, is the expansion of functionalities and the powering of complex self-evolving capabilities – as Hawking would put it – the'worst thing to happen to humanity?'

Artificial intelligence (AI) is being counted (pdf) among the hottest startup sectors in India this year, but the highly specialised space is struggling to grow due to the lack of a primary input: engineers. "Forget getting people of our choice, we don't even get applications when we advertise for positions for our AI team," said 25-year-old Tushar Chhabra, co-founder of Cron Systems, which builds internet of things (IOT)-related solutions for the defence sector. "It's as if people are scared of the words'artificial intelligence.' They start freaking out when we ask them questions about AI." India has over 170 startups focused purely on AI, which have together raised over $36 million. The sector has received validation from marquee investors like Sequoia Capital, Kalaari Capital, and business icon Ratan Tata.

A consumer's credit score used to be a commonly understood number -- the time-honored FICO score -- that banks all used in their underwriting. But banks increasingly are relying on dozens of scores that reflect a variety of data sources, analytics and use of artificial intelligence technology. The use of AI offers lenders the ability to get a precise look into someone's creditworthiness and score those previously deemed unscorable. But such scoring techniques also bring uncertainty: What it will take to convince regulators that AI-based credit scores are not a black box? How do you get a system trained to look at the interactions of many variables, to produce one clear reason for declining credit?

There's a growing sentiment out there with all the wonderful things happening in artificial intelligence, machine learning, and data science that these technologies are ready to solve all the things (including how to kill all humans). The reality is there are still a bunch of significant hurdles between us and the AI dystopia/utopia. One big one that is the main impetus behind my research is the disconnect between the statistical foundations of machine learning and how real data works. Machine learning technology is built on a foundation of formal theory. Statistical ideas, computer science algorithms, and information-theoretic concepts integrate to yield practical methods that analyze large, noisy data sets to train actionable and predictive models.

AI in Production – AI is only used by banks in production in a few key use cases such as high-frequency trading, fraud detection and credit scoring. In 2016 many machine learning R&D projects started across other business functions. In 2017 banks will move from testing machine learning models to putting models into production to make a real impact on business KPIs. Open-Source AI Platforms – Leading on from the last point, banks will have to consider if the best strategy for operationalizing models is to use a major cloud vendor, proprietary tech, open-source tech or in-house build. I think the winning combination is an open-source core machine learning platform supported by in-house R&D higher up the stack, and cloud provider focused mostly on the lower level compute tasks.

The focus on financial crime, and the money laundering that funds terrorist attacks and other criminal activities, has forced the industry to look for smarter approaches. In the previous posts in this mini-series, TJ Horan noted that AI is the newest hope for compliance, and Frank Holzenthal explored the benefits that AI can bring to compliance officers. Now it's my turn, and I'm going to explore the AI and machine learning technologies my team has integrated into the FICO TONBELLER Anti-Financial Crime Solutions. We have built on top of the FICO TONBELLER solutions using FICO's battle-proven and patented artificial intelligence and machine-learning algorithms, which are used in FICO Falcon Fraud Manager to protect about two-thirds of the world's payment card transactions. Industry experts have begun to realize the significance of analytics in combatting anti-money laundering.