If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Facebook seems to have a strategy of leveraging its capabilities in social marketing, AR & VR and interestingly, who would have thought of it, leveraging its advanced AI and deep learning capabilities to support the development of autonomous vehicles. Potential car buyers spend anywhere between 30 to 50 minutes every day on Facebook and that has helped the social business make significant inroads in digital prospecting and omni-channel commerce. Facebook believes that car companies are focusing more on the connected car, rather than the connected consumer. With every new customer car buying journey now beginning online, it is possible through Facebook's huge data on a customer's social behavior, to make that experience personalized and completely customized.
I'm Nathan Benaich -- welcome to issue #18 of my AI newsletter! I will synthesise a narrative that analyses and links important happenings, data, research and startup activity from the AI world. Grab your hot beverage of choice and enjoy the read! If you're looking to invest, research, build, or buy AI-driven companies, do hit reply and drop me a line. In a massive deal this quarter, Intel CEO agreed to purchase Mobileye for $15.3bn.
The greatest potential for deep learning is in adding business-relevant structure to less-structured, sense-like data -- such as images, audio and other sensor data. Generally when training machine learning algorithms (and deep nets are an extreme example of this), the more data the better. When it comes to the most broadly applicable deep learning problems -- object recognition in images, identification of people and their activities in video, natural language processing -- companies like Google and Facebook already sit atop a tremendous amount of relevant image, video, audio and text data. Thus, I expect artificial intelligence as a service (AIaaS) to be the dominant delivery vehicle for these high-value, broadly applicable use cases for deep learning.
Uber has taken another definitive step toward eliminating human drivers from its fleet of vehicles. This week, the ride service pioneer created a research division, known as AI Labs, while simultaneously acquiring machine learning startup Geometric Intelligence for an undisclosed sum. The new lab will become the incubator for products and technology that Uber can use to further automate the operation of its vehicles and service. The new lab will be initially populated by the 15 employees that made up Geometric Intelligence, a New York-based startup that assembled a team of young AI university researchers and enthusiasts. Heading the new Uber lab will be Geometric Intelligence founding CEO Gary Markus, a professor at NYU.
We pay a monthly subscription fee to J-Tech. In exchange, we're able to access their pool of hover vehicles on-demand. It knows where and when anyone needs to be at any given point in time and will pool the vehicle with anyone that fits into that schedule and destination. After a lengthy conversation, he excused himself to some quiet time.
Google DeepMind announced their research project with Moorfields Eye Hospital in London aimed at the early detection of preventable eye disease (e.g. I've tracked close to a dozen new startups founded in the last 12 months applying deep learning to medical imaging, such as BayLabs, Imagia, MD.ai, AvalonAI, Behold.ai, Apple plays feature catch-up with Google, namely on its photo tagging/search capabilities and predictive keyboard, but takes a view that privacy should come first. Following from this point, the New York Times features a piece on how algorithms perpetuate intrinsic biases in their training data, drawing on examples from the police force, image classification tasks and gender discrimination. Facebook's Language Technology team, which forms part of Applied ML, was the subject of a recent expose by Forbes diving into its various initiatives.