winners of the Google Cloud machine learning pitch-off. Microsoft’s new AI hub. Google acquires AI startup. http://ec2-35-176-202-215.eu-west-2.compute.amazonaws.com/news-briefing/.
Man, woman, and machine. In this connected world in which we live, technology allows us to connect with others in unprecedented ways, but it also comes with risks and challenges. Thankfully, tools such as Perspective (an API that makes it easier to host better conversations) have been created to deal with some of these challenges, such as flagging hateful and harmful speech.
J Nathan Matias (Visiting Scholar, MIT Media Lab & Center for Civic Media), in his recently defended thesis, presents an interesting perspective to this reality.
He argues that ‘in this computational culture, humans and machines continue to perpetuate deep-seated injustices. Our abilities to observe and intervene in other people’s lives also allow us to govern, forcing us to ask how to govern wisely and who should be responsible’.
He says that as we develop ways to govern behaviour through technology platforms, we have an opportunity to ensure that that the benefits will be enjoyed, questioned, and validated widely in an open society.
10 startups, pulled from a pool of 350+ applicants, presented onstage at Google’s Launchpad Space in San Francisco.
The startups vied for three prizes — a choice each from DCVC and Emergence, as well as a Built with Google award for the top startup utilizing Google’s Cloud Platform. Additionally, Google provided $200k in GCP credits to all finalists.
The Financial Conduct Authority, an independent UK financial regulatory body, is looking into the possible use of machine learning tools to enforce regulatory compliance.
Nick Cook, the FCA’s head of data and information operations, said that the regulator was “still learning”, and that feedback from a “call for input” from regulatory technology firms was informing it about how to support the adoption of automated, digitised compliance.
I saw a very interesting survey of 200 business leaders and 200 employees from four industries conducted by KRC Research, a unit of the Interpublic Group, in partnership with WorkMarket.
While most business leaders are interested or even welcoming of AI into their business, the majority (69%) of companies are not using. An additional 17% are unsure, while only 13% of companies said they are currently using AI. However, AI is on the horizon for many, with 43% of business leaders saying their organization likely will
implement any type of artificial intelligence in the next three to five years.
Microsoft Research AI (MSR AI) is a new organization that brings together the breadth of talent across Microsoft Research to pursue game-changing advances in artificial intelligence. The new research and development initiative combines advances in machine learning with innovations in language and dialog, human-computer interaction, and computer vision to solve some of the toughest challenges in AI.
A key focus for this initiative is to probe the foundational principles of intelligence, including efforts to unravel the mysteries of human intellect, and use this knowledge to develop a more general, flexible artificial intelligence. MSR AI pursues use of machine intelligence in new ways to empower people and organizations, including systems that deliver new experiences and capabilities that help people be more efficient, engaged and productive.
Sequence-to-sequence (seq2seq) models have enjoyed great success in a variety of tasks such as machine translation, speech recognition, and text summarization.
This tutorial gives readers a full understanding of seq2seq models and shows how to build a competitive seq2seq model from scratch. It focuses on the task of Neural Machine Translation (NMT) which was the very first testbed for seq2seq models with wild success. The included code is lightweight, high-quality, production-ready, and incorporated with the latest research ideas.
Halli Labs was founded with the goal of applying modern AI and ML techniques to old problems and domains — in order to help technology enable people to do whatever it is that they want to do, easier and better.
The company says it will be joining Google’s Next Billion Users team “to help get more technology and information into more people’s hands around the world.”
Clarifai, CogX award winner, has exciting news. They are releasing their mobile SDK, which gives users the power to train and use AI in the palms of their hands by installing machine learning capability directly on their devices, bypassing the traditional requirement of internet connectivity and massive computing power.
Clarifai’s Mobile SDK allows mobile devices to learn from and respond to their individual environment and user, thus creating the ultimate personalized user experience. Clarifai’s technology enables a whole
a generation of new product categories, transforms interactions between businesses and customers, and improves workflows across all industries.
Data hoarding is already well established as a defensive strategy among AI-centric companies. Google, Microsoft and others have open-sourced lots of software, and even hardware designs, but are less free with the kind data that makes such tools useful, but Luke de Oliveira, a partner at AI development lab Manifold and a visiting researcher at Lawrence
Berkeley National Lab, says that (as you might expect) such releases don’t usually offer much of value to potential competitors.
Progress on making machine learning less data hungry could upend the data economics of AI; Uber bought one company working on that last year. But right now it’s also possible to try and sidestep the AI incumbents’ usual data advantage. Rachel Thomas, cofounder of Fast.ai, says startups can find places to get rich applying machine learning outside the usual purview of internet giants, such as agriculture.
“Rather than going into an online travel agency and doing a search and seeing a list of 150 hotels, you enter in your profile what you’re looking for and a chatbot serves up a curated list of three to four in a messaging interface,” said Douglas Quinby, a senior vice president at the travel research firm Phocuswright. “The ideal is fewer options more tailored to your request.”
Most of these services are challenging the do-it-yourself system of browsing as offered by services like Expedia. New-wave
agents — human, robotic or a combination — will also allow users to continue a search over time, rather than start anew with a browser each session. Check out the article for the other companies, such as Pana and Mezi working on using AI to make build ‘data-driven travel agents’.