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“Should we introduce a rule that if you’re a political organisation, you may not target?” asked Sir Tim Berners-Lee, architect of the world-wide web, yesterday whilst speaking at the Innovate Finance Global Summit. He talked about some of his concerns for the internet over the coming years and envisioned a world where AI systems start to develop decision-making capabilities, and the impact this will have on the fairness of our economic systems. Could AI become the new masters of the universe by creating band running multitudes of companies better and faster than humans?
Article to Share with your Less Data Savvy Friends
Is all this effort is worth it? Kerry Liu, CEO of Rubikloud, a retail intelligence platform, argues that if you cut through the hype and use a strategic goal, machine learning can offer real-world value. The increased ease, speed, and functionality it offers create avenues for use cases across the spectrum of industries that rely heavily on data. For retail businesses, it provides an opportunity to improve and customize the customer experience.
China is pouring resources in to AI. The Chinese leadership singled out AI as a key area of development in a report released during the National People’s Congress in March. According to iResearch, the market for AI-related services will grow more than $1 billion from $203 million by 2020. China has already made real progress. In 2015, the country surpassed the U.S. with the number of published journals related to deep learning, a branch of AI that specializes in teaching machines to learn by themselves. Additionally, Alibaba, Baidu and Tencent
have all announced plans for AI laboratories and projects worth billions of dollars.
Federated Learning approach is all about decentralizing the work of artificial intelligence. Instead of collecting user data in one place on Google’s servers and training algorithms with it, the teaching process happens directly on each user’s device. Essentially, your phone’s CPU is being recruited to help train Google’s AI. Will this open a way for new algorithms and privacy in the Digital Era?
Far to the North of Silicon Valley, Canadian Finance Minister Bill Morneau’s new budget allocates $125 CAD Million to support AI research in the form of public-private partnerships. While few would immediately associate Canada and AI, several researchers (Geoffrey Hinton, Yoshua Benigo and Richard Sutton for example) from Canadian institutions have developed frameworks and solution that now predicate many of the advances we are seeing. Can Canada start to capitalize on its homegrown and historic talent?
Last Friday, Okta, a provider of a secure process that customers use to sign in to cloud services, raised $187m and its stock soared 38% on its first day of trading. Wall Street investors are starved for growth. However, few tech companies are ready to take advantage of this trend. High valuations in the private market, which created a swath of “unicorns” valued at more than a billion each, left little incentive for the tech starts-ups to do painstaking work of preparing for an IPO.
Here are 5 examples of MLaaS (Machine learning as a Service) that can be integrated into that app idea you had. This overview looks at the services that exist which can enable developers to begin connecting their own apps and IoT devices to voice recognition, chatbots and artificial intelligence. Focus on the product and let someone else do the maths… Go on. Get building.
We humans are capped at perceiving just 3 dimensions at a time but many datasets we encounter today are higher dimensional. How can we harness the incrediblepattern-recognition superpowers of our brains to visualize complex and high-dimensional datasets?