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In-Q-Tel Invests in AI Tools Developer Fiddler; Krishna Gade Quoted


A.J. Bertone, partner at In-Q-Tel, said in a statement published Thursday Fiddler's platform works to ensure that AI and machine learning systems …

Get smart! Applying machine learning on operational data


Will the factories of the future be driven by artificial intelligence and … Predictive analytics and machine-learning-based modeling can address …

Public must help guide future of artificial intelligence, expert say


Artificial intelligence simulates human thought, using machines programmed to reason like humans and mimic their actions.

The robots are already among us


Andrea Thomaz founded Diligent Robotics in 2016, using what she learned building earlier robot Poli to create a robot aide for nurses called Moxi, so they can focus on patient care rather than restocking supplies, delivering medications or samples, and shuffling around equipment. AI gives Moxi autonomy to navigate hospital halls, while a dexterous arm can recognise and grab objects – all with a smile on its LED face, as this socially-aware robot has facial expressions to set patients at ease.

[D] has anyone ever worked on a machine learning model for "queues"?


Has anyone ever worked on a machine learning model for "queues"? Suppose there is a bakery: the bakery has has "n" people working, "m" people in line" and "q" orders that they are currently working on. The bakery is interested in making a machine learning model that predicts how long a customer will have to wait before the customer's order is ready and how long will the next customer have to wait before they can place an order. Has anyone ever come across a machine learning model which can predict waiting and processing times? I have seen examples online where people try fitting exponential distributions to historical waiting times and see how well they fit, as well as trying different m/m/k combinations... but has anyone ever come across an instance where machine learning algorithms (e.g.

[D] Invitation to help address AI misrepresentation and misconceptions


TLDR: I run a site to debunk misconceptions of AI news, pls positive response, so hope bringing it up again now that we could use more help is fine. As I posted before, for more than 3 years I've been running this thing called Skynet Today (the name is meant to be ironic/news-y), with the mission of "Putting AI News In Perspective", or in other words debunk inaccurate portrayals of AI research in media and also put out articles that put things in perspective. As many people here are researchers and feel annoyed at hype/misconceptions about AI, I wonder if any of you might want to join our effort. We are basically a couple of grad students doing this in our spare time, and have not put out any new articles in a while due to being busy / not having much help (writing is a lot of work!). If interested, please consider taking a look at our contribution survey, or just message me.

Developers Turn To Analog For Neural Nets


Machine-learning (ML) solutions are proliferating across a wide variety of industries, but the overwhelming majority of the commercial implementations still rely on digital logic for their solution. With the exception of in-memory computing, analog solutions mostly have been restricted to universities and attempts at neuromorphic computing. However, that's starting to change. "Everyone's looking at the fact that deep neural networks are so energy-intensive when you implement them in digital, because you've got all these multiply-and-accumulates, and they're so deep, that they can suck up enormous amounts of power," said Elias Fallon, software engineering group director for the Custom IC & PCB Group at Cadence. Some suggest we're reaching a limit with digital. "Digital architectural approaches have hit the wall to solve the deep neural network MAC (multiply-accumulate) operations," said Sumit Vishwakarma, product manager at Siemens EDA. "As the size of the DNN increases, weight access operations result in huge energy consumption." The current analog approaches aren't attempting to define an entirely new ML paradigm. "The last 50 years have all been focused on digital processing, and for good reason," said Thomas Doyle, CEO and co-founder of Aspinity.

Agencies struggle to find the right AI solutions -- GCN


Three-quarters of government decision-makers struggle to select the right artificial intelligence solutions for their projects, a new report found. Still, 61% of respondents to a KPGM survey said AI is moderately to fully functional in their organization, according to "Thriving in an AI World," a report the professional services firm released March 9. And in the next two years, respondents said they plan to use AI to improve process automation (48%) and analytics (40%). To determine the best AI solutions, agencies must first define their use case, said Rob Dwyer, KPMG advisory principal specializing in technology in government. Robotic process automation is a common entry point to AI in the public sector because vendors in that area are well established, and it's relatively easy to earn small wins that can drive support for other AI efforts, he said.

Autonomous Driving, AI System on a Chip, Drug Discovery Firms Among Top Funded - AI Trends


The top-funded companies on the recently-released list of top 100 most-promising AI companies to watch from CBInsights, a market intelligence company based in New York, include companies offering autonomous driving software, an AI System on a chip, endpoint security with AI, and a drug discovery company. The list, selected from a base of 6,000 companies, is based on business relations, investor profile, news sentiment analysis, R&D activity, a proprietary scoring system, market potential, competitive landscape, team strength and tech novelty, according to an account in TechRepublic. "This year's cohort spans 18 industries, and is working on everything from climate risk to accelerating drug R&D," stated CB Insights CEO Anand Sanwal. Companies on last year's list went on to raise $5.2 billion in additional financing, including 16 of over $100 million each. Some companies exited via merger or acquisition, IPOs or SPACS.