We're thrilled to have Chaney Ojinnaka, CEO of VendorMach and VP of Enterprise, Ivan Kotorov in as guest speakers for Byte Academy students and a limited number of members. VendorMach is a network driven platform that uses AI (predictive analytics) to produce the VM Trust score. The score is then used to offer invoice receivables financing and insurance products to SMBs while enabling enterprises to better manage their supply chain. Founded in London, VendorMach recently opened its US office in New York. Topics covered over the evening will include: VendorMach and founders' overview, building a startup, AI and machine learning, data science in fintech, supply chain finance, FinTech environment London v. US (pre/post Brexit), pros & cons of an accelerator and more.

On the morning of March 9th at the Four Seasons Hotel in Seoul, South Korea, the world changed forever. A computer program called AlphaGo used the relatively new social science of cognitive computing to strategize and beat one of the top masters in the world at the ancient Chinese board game Go. It was hailed as one of the most defining moments in the development of artificial intelligence so far. At NETSCOUT, we watched the development of the AlphaGo program, originally created by British company DeepMind before being acquired by Google in 2014, with great interest. Cognitive computing is something we do every day at NETSCOUT to ensure that our solutions deliver the digital strategies of our clients with maximum efficiency. We like to sometimes compare ourselves to conductors helping to keep the trains of digital networks running on time, and while that is an accurate description of our service assurance platform, it doesn't really dive deeply into the intelligence that our products provide.

To tackle my aunt's puzzle, the expert systems approach would need a human to squint at the first three rows and spot the following pattern: The human could then instruct the computer to follow the pattern x * (y 1) z. Even when machines teach themselves, the preferred patterns are chosen by humans: Should facial recognition software infer explicit if/then rules, or should it treat each feature as an incremental piece of evidence for/against each possible person? And so they designed deep neural networks, a machine learning technique most notable for its ability to infer higher-level features from more basic information. These questions have constrained efforts to apply neural networks to new problems; a network that's great at facial recognition is totally inept at automatic translation.

A few months ago, my aunt sent her colleagues an email with the subject, "Math Problem! She thought her solution was obvious. Her colleagues, though, were sure their solution was correct--and the two didn't match. Was the problem with one of their answers, or with the puzzle itself? My aunt and her colleagues had stumbled across a fundamental problem in machine learning, the study of computers that learn. Almost all of the learning we expect our computers to do--and much of the learning we ourselves do --is about reducing information to underlying patterns, which can then be used to infer the unknown.

It's not just gigantic search engines that are looking to acquire tech and talent around emerging areas like speech recognition. Even design and craft marketplaces can use a little machine learning and artificial intelligence to make their wheels turn a little better. Today, the popular handmade-goods site Etsy announced it has acquired a startup called Blackbird Technologies, which developed algorithms for natural language processing, image recognition and analytics -- similar to those used by Amazon and Google for product and other searches -- and then "democratized" them to be used by any company of any size. At Etsy, the tech will be used to improve its own search features. Financial terms of the deal have not been disclosed, but we're asking and will update if we learn more.

As artificial intelligence (AI) continues to move from the summer of hype to the fall tech conference news cycle, mass confusion has begun on what AI can be used for. From fears of SKYNET, to hopes for the computer in StarTrek and Jarvis in Iron Man, the value will come from defining the proper outcomes. AI is more than just a fad. With a market size of 100B by 2025, Constellation sees the AI subsets of machine learning, deep learning, natural language processing, and cognitive computing taking the market by storm (see Figure 1). The disruptive nature of AI comes from the speed, precision, and capacity of augmenting humanity.

Google isn't the only tech giant hoping that artificial intelligence can aid the fight against cancer. Microsoft has unveiled Project Hanover, an effort to use AI for both understanding and treating cancers. To begin with, the company is developing a system that would automatically process legions of biomedical papers, creating "genome-scale" databases that could predict which drug cocktails would be the most effective against a given cancer type. An ideal treatment wouldn't go unnoticed by doctors already swamped with work. Microsoft is also teaming with the Knight Cancer Institute on AI that would personalize those drug mixes on a patient-by-patient basis.

A few months ago, my aunt sent her colleagues an email with the subject, "Math Problem! She thought her solution was obvious. Her colleagues, though, were sure their solution was correct – and the two didn't match. Was the problem with one of their answers, or with the puzzle itself? My aunt and her colleagues had stumbled across a fundamental problem in machine learning, the study of computers that learn.

Machine learning can predict strains of bacteria likely to cause food poisoning outbreaks, research has found. The study – which focused on harmful strains of E. coli bacteria – could help public health officials to target interventions and reduce risk to human health. Researchers at the University of Edinburgh's Roslin Institute used software that compares genetic information from bacterial samples isolated from both animals and people. The software learns the DNA signatures that are associated with E. coli samples that have caused outbreaks of infection in people. It can then pick out the animal strains that have these signatures, which are therefore likely to be a threat to human health.

Steve recognises the "disruptive and pervasive" impact AI is already having on business: "AI is enabling companies to achieve improved operational efficiency, develop new and improved products and services, and most significantly entirely new business models. Universities are particularly well suited for interdisciplinary approaches that include multiple technical disciplines as well as the liberal arts, humanities, arts, and social sciences. "Data sharing agreements with appropriate protections for sensitive confidential information enable university data science researchers to develop practical algorithms using real-world data. Municipal, state, and national governments are working to improve accessibility and the democratization of data.