I have been building technology start-ups since 2003. Throughout the years I observed a trend towards the commoditization of machine learning algorithms and the data wrangling tools to deploy these techniques in the real world. The team at Seldon had been hand-crafting recommendation algorithms for a number of years. We adopted Hadoop back in 2011 in order to scale our data processing capabilities beyond programmatic and relational databases. Hadoop had a sister called project Apache Mahout that bundled a variety of machine learning algorithms.

Machine learning is an enigma to most. For decades it's a been a field dominated by scientists and the few organizations with enough computing power to run complex algorithms against huge datasets. But now the world of machine learning and predictive analytics is opening up to developers and companies of all sizes, with machine learning (ML) providers offering their products through a subscription-based model or open sourcing some of their technology. Data scientists are growing in number, but only in the tens of thousands... There may not be enough to go around.

You could see that it has 13 categories, 910 companies, and an average funding of 10 Million per company. At Venture Scanner, we are currently tracking over 910 Artificial Intelligence companies in 13 categories across 63 countries, with a total of 3.68 Billion in funding. To see the full list of 910 Artificial Intelligence startups, contact us using the form on www.venturescanner.com. Venture Scanner enables corporations to research, identify, and connect with the most innovative technologies and companies. We do this through a unique combination of our data, technology, and expert analysts.

After the launch of Project loon, Google has been using static algorithms to make its internet balloons change altitude and stay in position for several years. Those hard coded algorithms were confined to adapt to unexpected weather patterns, which are pretty common in a place like the stratosphere. Well, those situations are history now. The Project Loon team has revealed that their engineering team is moving away from those hard coded control algorithm, instead, they are using machine learning to alter those internet balloon's behavior and make them stick to the desired flight path much longer. The company has already launched a test balloon into the stratosphere over Peru, which stayed there for 98 days, adapting to difficult wind conditions that might have sent it floating away.

Virtual reality may be generating most of the buzz today, but another major tech shift looms much closer on the horizon: machine learning. The technology has already made inroads with the public through platforms such as Amazon's Echo and Google's Deep Dream Generator. But its influence will extend beyond voice-controlled speakers and AI-enhanced art, effecting a sea of change for businesses of all sizes. It will be a few years before we witness machine learning's breakthrough moment, but it's coming -- and it will change everything. Humans could be incredibly effective given endless timelines, budget, and energy.

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Microsoft researchers are doing a bug bash on cancer, complete with software code names like "Project Hanover." Some of them are actually drilling down into our genetic code, looking for ways to reprogram the immune system to combat cancer cells more effectively. "If you can do computing with biological systems, then you can transfer what we've learned in traditional computing into medical or biotechnology applications," Microsoft's Neil Dalchau says in the company's in-depth report about its cancer moonshots. Others are enlisting the power of cloud computing to identify which treatment would work best for a particular cancer patient, based on his or her personalized medical profile. Microsoft and AstraZeneca are already using a software tool known as the Bio Model Analyzer to figure out why leukemia patients respond differently to different treatments.

Tonya Riley of Inverse reports that artificial intelligence is already well on its way to being the future of food service, but what if it could also do things like prevent foodborne illnesses, such as E. coli? Researchers at University of Edinburgh say they've designed software to do just that. The A.I. compares the genetic signatures of E. coli samples that have caused infection in humans to bacterial samples from humans and animals. The technology will allow researchers to identify deadly strains of E. coli before the threat becomes an outbreak. "Our findings indicate that the most dangerous E. coli O157 strains may in fact be very rare in the cattle reservoir, which is reassuring," University of Edinburgh Professor David Gally said in a press release.

Yes, you read that right: an actual artificial intelligence created the advertisement for a movie about terrifying AI. To create the film, the company used experimental Watson APIs and machine learning techniques to comb through hundreds of movie trailers for horror and thrillers. "Let's send Watson to film school," as John Smith, an IBM fellow who helped work on the project, explained. The team behind Watson helped the AI learn how movie trailers work, and then analyzed every scene in the human-made movie to pick the best ones for the trailer. The AI was able to detect which of the movie scenes were cheerful and uplifting, versus which ones were sad or scary.

The event took place on Thursday, September 22nd, 2016. We asked startup vendors and representatives from the organization team about these trends. Kevin Kelly was right when he predicted that the business plans of the next 10,000 startups were easy to forecast: »take X and add AI«. Computers that see and listen, think and predict are already making a difference across industries. Artificial intelligence can automate processes, reduce costs and improve customer experience. Corporations need to leverage those machine learning technologies or risk being replaced by'smarter disruptors.' [Tomasz Rudolf, CEO D-RAFT] For sure, we now have the technology (measured in computer power and algorithms) that is able to achieve great progress every year. But the most important difference is that AI started to finance itself. A great recent example is about using DeepMind's work on reinforcement learning to reduce Google's Data Center cooling bill by 40%. The biggest difference is unsupervised learning and the availability of "cheap" GPU power. That's why we see so many startups rising in the field of AI. I believe this is the main reason that we are entering the machine learning era.