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) …
The 100 startups on our list have raised $11.7B in aggregate funding across 367 deals since 2012. Today, CB Insights unveiled the second annual AI 100 -- a list of 100 of the most promising private companies applying artificial intelligence algorithms across 25 industries, from healthcare to cybersecurity -- at the A-Ha! conference in San Francisco. The companies were selected from a pool of 2,000 startups based on several criteria, including investor profile, tech innovation, team strength, patent activity, mosaic score, funding history, valuation, and business model. The market map below categorizes the AI 100 companies based on their industry focus. Please click on the image to enlarge.
If culture is a set of beliefs and behaviors that people pass from one generation to another, then is artificial intelligence becoming one? AI is certainly changing how people think about and interact with technology. Talking to your phone and expecting an intelligent response has quickly shifted from novelty to routine expectation. Self-driving vehicles orienting themselves in the physical world will soon shuttle us all over, changing how millions of commute hours are spent and our relationship with cars themselves. Inherent in AI is a capacity to learn, and to use this knowledge to advance the tasks it's been assigned to tackle.
The Robot Launch global startup competition is over for 2017. We've seen startups from all over the world and all sorts of application areas – and we'd like to congratulate the overall winner Semio, and runners up Apellix and Mothership Aeronautics. All three startups met the judges criteria; to be an early stage platform technology in robotics or AI with great impact, large market potential and near term customer pipeline. Semio from Southern California is a software platform for developing and deploying social robot skills. Ross Mead, founder and CEO of Semio said that "he was greatly looking forward to spending more time with The Robotics Hub, and is excited about the potential for Semio moving forward."
At the end of 2017, there will be 8.4 billion connected things in use worldwide up 31 percent from 2016, and this figure is expected to reach 20.4 billion by 2020. When Internet of Things (IoT) as an industry took off in India, it spawned a host of startups selling edge devices that could gather and crunch data from corporate customers. These startups ran into one fundamental problem, which was data lifting. The data was so voluminous that these startups took so much time to organise them that they ran out of money to keep the companies afloat. In the end, their services were just organising data for customers with very little insights.
For the Winter '17 cohort in collaboration with Macquarie Group, over 800 companies were vetted and 10 were accepted. The selected scaleups cover a range of technology verticals applicable to all enterprise. Collectively, the scaleups have raised £40m to date, have between 10 and 40 employees each and have an incredible breadth of enterprise clients -- ranging from Airbnb to Aston Martin to Barclays to Compass Group to L'Oreal to Wells Fargo. Collecting leads at events is a broken process. We're here to fix it.
Unlike tennis balls and musketeers, AI ventures don't typically come in threes. The former AI leader at both Google and Baidu has been on an entrepreneurial spree, making his third major announcement in recent months to launch Landing.ai. The startup will help make large manufacturing companies more efficient by using artificial intelligence, according Ng's blog post today (Dec. He also serves as chairman of Coursera, the online education company he cofounded in 2012. Ng writes that his startup and Foxconn will "jointly develop and deploy AI solutions and training globally."
Speaking of fraud prevention: As we discussed in our introduction, quite a few companies are slapping AI and machine learning on their websites, claiming they are using proprietary algorithms to cure everything but cancer. We're not suggesting the startups here are selling snake oil. Many are well funded by respectable firms and companies. Of course, so was Juicero whose investors included (ahem) Campbell Soup and Google. Just as we were putting the finishing touches on this article, we came across a Fast Company story that lists at least another four companies reportedly applying proprietary algorithms to personalized health and nutrition based on genetics and other health data.
Gargantuan Taiwanese manufacturer Foxconn employs more than 1 million people and tens of thousands of robots making iPhones and other electronics. It has a reputation for cost cutting, including at the expense of its workers. Now, it's teaming up with an artificial-intelligence researcher who helped trigger Google's reorientation around machine learning in order to make its own factories more efficient. Andrew Ng was a Stanford professor when he joined Google in 2011 to work on a project that created software able to recognize cats--and a new corporate emphasis on AI at Google. He later led AI research at Chinese search engine Baidu.
With virtual assistants answering our emails and robots replacing humans on manufacturing assembly lines, mass unemployment due to widespread automation seems imminent. But it is easy to forget amid our growing unease that these systems are not "all-knowing" and fully competent. As many of us have observed in our interactions with artificial intelligence, these systems perform repetitive, narrowly defined tasks very well but are quickly stymied when asked to go off script -- often to great comical effect. As technological advances eliminate historic roles, previously unimaginable jobs will arise in the new economic reality. We combine these two ideas to map out potential new jobs that may arise in the highly automated economy of 2030.
Chattermill, a London-based startup that uses'deep learning' to help companies make better sense of customer feedback, has raised £600,000 in seed funding. Backing comes from Entrepreneur First -- Chattermill is an alumnus of the company builder -- and Avonmore Developments, along with a number of angel investors, including Jeff Kelisky, CEO of Seedrs. Founded in 2015 by friends Mikhail Dubov and Dmitry Isupov, Chattermill is one of a number of startups that are tackling the problem of how to sift through and respond to customer feedback and across multiple channels. With that data growing exponentially, the company is employing deep learning to help do the job in, arguably, a much more scalable and potentially more accurate way. "We help companies understand and improve their customer experience: we give companies insight that helps them craft better products and services," Dubov, Chattermill's CEO, tells me.