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Applying machine learning to keep employees safe and save lives


Whether on factory floors, construction sites, or warehouses, accidents have been an ongoing, and sometimes deadly, factor across industries. Add in the pandemic -- and an increasing rate and intensity of natural disasters -- and the safety of employees and citizens becomes more complicated. Australian-based Bigmate, a computer vision company focused on enhancing workplace safety, is using machine learning to reduce workplace accidents, help companies detect potentially ill employees as they arrive on site, and aid organizations in the operational management of natural disasters. Bigmate's risk management and computer vision expertise combined with their long-term experience in asset management are all supported by their in-depth knowledge of advanced AWS Services to maximize operational turnaround. "Organizations are deeply concerned about safety, and are looking to what AI and ML can bring to the table, not for the sake of technology but to help improve safety in the workplace through targeted applications with clear benefits."

Datasaur snags $3.9M investment to build intelligent machine learning labeling platform


As machine learning has grown, one of the major bottlenecks remains labeling things so the machine learning application understands the data it's …

Using Artificial Intelligence To Tame The Most Volatile Asset Class In History


We are currently living through one of the most turbulent, financial, technological, and social periods of our lives. This year has brought forward mass global economic uncertainty, unlike anything we have seen before. As a result, monetary policies and their custodians are attempting to adjust without clarity on how decisions will impact both the near and long term future. Financial markets and monetary policies across the globe are unstable and their futures are viewed with broad skepticism. Many of us look to market or economic "experts" for direction, however, we appear to be lacking modern economic precedence to put our current circumstances into the proper context.

Datasaur snags $3.9M investment to build intelligent machine learning labeling platform – TechCrunch


As machine learning has grown, one of the major bottlenecks remains labeling things so the machine learning application understands the data it's working with. Datasaur, a member of the Y Combinator Winter 2020 batch, announced a $3.9 million investment today to help solve that problem with a platform designed for machine learning labeling teams. The funding announcement, which includes a pre-seed amount of $1.1 million from last year and $2.8 million seed right after it graduated from Y Combinator in March, included investments from Initialized Capital, Y Combinator and OpenAI CTO Greg Brockman. Company founder Ivan Lee says that he has been working in various capacities involving AI for seven years. First when his mobile gaming startup, Loki Studios was acquired by Yahoo! in 2013, and Lee was eventually moved to the AI team, and most recently at Apple.

Sentinel loads up with $1.35M in the deepfake detection arms race – TechCrunch


Estonia-based Sentinel, which is developing a detection platform for identifying synthesized media (aka deepfakes), has closed a $1.35 million seed round from some seasoned angel investors -- including Jaan Tallinn (Skype), Taavet Hinrikus (TransferWise), Ragnar Sass & Martin Henk (Pipedrive) -- and Baltics early-stage VC firm, United Angels VC. The challenge of building tools to detect deepfakes has been likened to an arms race -- most recently by tech giant Microsoft, which earlier this month launched a detector tool in the hopes of helping pick up disinformation aimed at November's U.S. election. "The fact that [deepfakes are] generated by AI that can continue to learn makes it inevitable that they will beat conventional detection technology," it warned, before suggesting there's still short-term value in trying to debunk malicious fakes with "advanced detection technologies." Sentinel co-founder and CEO Johannes Tammekänd agrees on the arms race point -- which is why its approach to this "goal-post-shifting" problem entails offering multiple layers of defence, following a cybersecurity-style template. He says rival tools -- mentioning Microsoft's detector and another rival, Deeptrace, aka Sensity -- are, by contrast, only relying on "one fancy neural network that tries to detect defects," as he puts it.

Rethinking The Way We Benchmark Machine Learning Models


"Unless you have confidence in the ruler's reliability, if you use a ruler to measure a table, you may also be using the table to measure the ruler." Do machine learning researchers solve something huge every time they hit the benchmark? If not, then why do we have these benchmarks? But, if the benchmark is breached every couple of months then research objectives might become more about chasing benchmarks than solving bigger problems. In order to address these challenges, researchers at Facebook AI have introduced Dynabench, a new platform for dynamic data collection and benchmarking.

The technology that powers the 2020 campaigns, explained

MIT Technology Review

Campaigns and elections have always been about data--underneath the empathetic promises to fix your problems and fight for your family, it's a business of metrics. If a campaign is lucky, it will find its way through a wilderness of polling, voter attributes, demographics, turnout, impressions, gerrymandering, and ad buys to connect with voters in a way that moves or even inspires them. Obama, MAGA, AOC--all have had some of that special sauce. Still, campaigns that collect and use the numbers best win. That's been true for some time, of course.

Using AI to grow your business and create enterprise value


Investing in AI can help a business grow, while generating enterprise value. In this article, five experts provide their advice on how businesses can use AI to improve their business and products. "Businesses that deploy AI can expect sales growth through more precisely targeted and relevant customer engagements, more rapid scalability across business operations and greater productivity," says John Michaelis, an expert in the practical aspects of using AI and an experienced business consultant. He is also an active angel investor and board advisor for early-stage AI companies. He provides three essential tips for using AI to grow your business and generate enterprise value.

Top 8 AI Influencers To Follow On Instagram


One of the popular visual social media sites, Instagram has gained much traction over the last few years. Today, Instagram is not just a platform for sharing photos and videos, but it has also become an excellent platform for sharing educational information. Millions of people use Instagram with a majority of them being youths or young adults. Increase in the popularity and relevance of social media platforms has led the educators and other professionals to choose social network sites such as Instagram as a source to deliver educational contents. The platform includes short, crisp as well as informative posts on emerging technologies like artificial intelligence, machine learning, data science and more. It has indeed become a parallel world of Data Science and AI enthusiasts on Instagram.

Radical AI podcast: featuring Veena Dubal


Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to Veena Dubal about the ethical crisis of the gig economy. What is precarious work and how does it impact the psychology of labor? How might platforms like Uber and Lyft be negatively impacting their workers? How do gig economy apps control the lives of those who use them for work?