Allen Institute For AI (AI2) Launches The 2.7.0 Version of AI2-THOR That Enables Users To Reduce Their Training Time Dramatically

AI2-THOR

Allen Institute for AI (AI2) has recently announced the 2.7.0 release of AI2-THOR. AI2-THOR is an open-source interactive environment for training and testing embodied AI. The 2.7.0 version of AI2-THOR contains several performance enhancements that can provide dramatic training time reductions. The new version introduces improvements to the IPC system between Unity/Python and serialization/deserialization format. It includes new actions that are much better to control the metadata. 

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Hands-on to ReAgent: End-to-End Platform for Applied Reinforcement Learning

Facebook ReAgent, previously known as Horizon is an end-to-end platform for using applied Reinforcement Learning in order to solve industrial problems. The main purpose of this framework is to make the development & experimentation of deep reinforcement algorithms fast. ReAgent is built on Python. It uses PyTorch framework for data modelling and training and TorchScript for serving.

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Artificial intelligence in Journalism

AI Journalism

Artificial intelligence in journalism is now a reality. Artificial intelligence has entered almost all aspects of our lives, including journalism. Due to the advent of digital media, we unknowingly consume content based on artificial intelligence everywhere. Whether it’s YouTube’s recommended videos, your Facebook feed, or the kinds of advertisements you see on regular websites, they are all specially catered to you with the use of AI.

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Peak expands access to AI ‘Decision Intelligence’

AI Decision Intelligence

AI deployments are steadily migrating to retail applications as commercial brands seek to get a better handle on supply chains along with marketing and sales.
Increased demand for embedded AI software used to guide those decisions is on the rise, benefitting early movers offering automation tools designed to inform decisions on logistics and marketing. Among them is U.K.-based Peak AI, which this week announced a $21 million Series B venture round, bringing its total investor funding to $43 million.

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NASA’s latest Mars Rover, Perseverance, will get help from AI on the Red Planet

Mars rover Perseverance

As NASA’s latest Mars rover, Perseverance, prepares to land on the Martian surface Feb. 18 (Thursday) after a six-month flight to the red planet, the use of AI will quickly become an important part of the rover’s mission once it begins to traverse the landscape. As Perseverance jumps into its goal of searching for traces of microscopic life on Mars that dates back billions of years, the rover will use an AI-powered device…

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Roadmap to Artificial Intelligence Adoption — Understand AI

AI text with head

Still, not all industries are using Artificial Intelligence. Implementing AI in a company is not a super easy task, but let’s be honest, it is not even complicated. Probably, the main barrier to adoption is the company culture and the inclination towards a digital adaptation. It is not just a matter of technology!

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AI Product Manager

Workers at a Board

I recently completed the Artificial Intelligence Product Manager Nanodegree Program on Udacity and I’d like to share a summary of everything I learned with you. This also includes bits from my experience as a technical product manager. This all a huge dump from my mind, written from the first stroke to last on my keyboard so kindly excuse any details I may miss or depths I didn’t hit.

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Scaling for Robot Intelligence

Robot intelligence

RIOS CorporationJust now·3 min readBy Matt ShafferTechnologically, the last 30 years or so have been shaped by advancements in computation, and the ability to build machines that can make decisions independent of human operators is a direct result of this progress. With the growing global demand for machines that can perform labor, intelligent automation will bring about the real changes needed to deliver at scale. Though historically, robotic systems with embedded intelligence are inherently more difficult to build with reliability because they operate in the real world — a world with less regularity and more unpredictable consequences than the carefully-designed frameworks of the digital world. Given the challenges, it is not surprising to consider that factory automation is still largely driven by human workers who perform tasks that are often repetitive, but difficult to automate.Machine learning is most effective at scale, where the experiences of many systems can be aggregated.Automation is non-trivial, but it is not due to the fact that research cannot solve a lot of these problems — but that it only became a possibility more recently. There are certainly quite a few reasons for this, some of which have to do with the hardware and computational advancements, and others that revolve around data. But there is another interesting theory going around that is articulated by Sara Hooker in “The Hardware Lottery”. She postulated that research directions in the field of machine learning are often explored due to software and hardware available at the time, rather than being motivated by the most promising ideas. This theory is aligned with our premise at RIOS that advancing the capabilities of robots is heavily dependent on both specialized hardware and software that must coevolve.Robots in the real world have traditionally been programmed in isolation on a single task, rather than leveraging collective knowledge as in simulated environments..Today, we are reaching an inflection point, and there is a monumental opportunity to develop custom hardware and software systems that enable robots to take on increasingly open-ended tasks without the need of reprogramming for each new instruction. We can do this by taking the lessons of the internet to apply data at scale to robotics. By strategically designing systems with the intent of learning from them, and building the infrastructure to support information sharing, we can adapt more quickly to new tasks and master the ones we are already familiar with. The real promise of applying machine learning to robotics is not teaching a single robot to learn for itself, but to aggregate experience from a vast network of robots so that they can improve at scale.A core tenet of what we do at RIOS is to build machines with this idea in mind. Like hardware, skills and behaviors should be transferable across platforms when possible, and each deployed system should be able to share what it has learned with other systems. At a high level, you can think of this as storing knowledge rather than just data to reduce the need for retraining. The result is a class of robots that can do a variety of tasks and address new challenges with less development time. By building distributed robots that continuously learn from both their environment and the collective experience of others, we can help push intelligent robotics forward at scale much in the same way that the information economy benefited from the web.The next generation of technological progress is starting to favor organizations that can rapidly assemble the best technologies of the web-era and use them to take fields like robotics in new directions. In many parts of the world, where labor shortages exist or workers are subjected to poor conditions, this couldn’t come at a better time. Moreover, what used to be a long lead-time in deploying new systems or developing solutions is disappearing as robots can reuse not just hardware, but prior knowledge when taking on new tasks. As more robots fill empty roles in factories, we’ll start wonder how we lived without them, and eventually forget they are doing the most thankless of work for us without any complaints.Matt Shaffer is the RIOS Director of Artificial Intelligence and is the architect behind the brain of our robots. This article is a shortened version of Matt’s article Scaling Artificial Intelligence for Robotics in 2021.

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Bias in Machine Learning Algorithms

The progress made in the field of machine learning and its capabilities in solving practical problems have heralded a new era in the broader domains of artificial intelligence (AI) and technology. Machine learning algorithms can now identify groups of cancerous cells in radiographs, write persuasive advertising copy and ensure the safety of self-driving cars.

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GPT-2 vs GPT-3: The OpenAI Showdown

Woman at a blackboard

GPT-2 vs GPT-3: The OpenAI Showdown. The Generative Pre-Trained Transformer (GPT) is an innovation in the Natural Language Processing (NLP) space developed by OpenAI. These models are known to be the most advanced of its kind and can even be dangerous in the wrong hands. It is an unsupervised generative model which means that it takes an input such as a sentence and tries to generate an appropriate response, and the data used for its training is not labelled.

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Boast.ai secures $100 million to help startups claim R&D tax credits

Boast.ai Platform

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Boast.ai, a company developing software that automates the process of claiming R&D tax credits, today announced that it secured a $100 million credit facility from Brevet Capital. The company plans to use the loan to further develop its platform as it looks to expand its startup customer base.
Each year, the federal governments of the U.S. and Canada provide more than $15 billion in innovation incentives to private companies. In fact, it’s estimated that nearly a third of U.S. patents rely directly on U.S. government-funded research. But filling out the applications to receive this funding is often a cumbersome process — and yet another barrier for companies squeezed by the pandemic as venture capitalists pull back on early-stage investments.

Boast.ai, which has offices in Vancouver, Toronto, and Calgary, Canada, in addition to its San Francisco headquarters, was cofounded in 2011 by Alex Popa and Lloyed Lobo. Popa previously managed the R&D tax practices for large accounting firms, while Lobo formerly ran product and growth for a number of venture-backed tech startups.
Boast.ai aims to automate the government grant application process with a machine learning-powered tax credit platform. The company’s software and teams of accountants gather data from existing technical and financial systems to help identify, categorize, and time-track eligible projects, with the goal of larger claims, lower risk, and less time invested from teams. Among the data sources Boast.ai draws on are payroll, development platforms like GitHub and Jira, and accounting, which its systems leverage to produce time-tracking documents and tax forms, along with audit evidence, timelines, and balance sheets.

Boast.ai specifically targets three types of tax credits: the U.S. Research & Experimentation Tax Credit (R&D Tax Credit) and the Canadian government’s SR&ED Tax Credits (SR&ED) and Interactive Digital Media Tax Credits (IDMTC). The R&D Tax Credit, which was made permanent in 2015, is a general business tax credit for companies that incur R&D costs germane to salaries, subcontractor-related expenses, and materials in the U.S. The SR&ED is a program that covers up to 70% of R&D expenditures for creating or improving existing products, processes, principles, methodologies, and materials in Canada. As for the IDMTC, it provides a refundable 17.5% tax credit on eligible salaries and wages for Canadian employees involved in producing video games, educational software, entertainment software, and simulators.
The 35-employee Boast.ai claims to have helped secure over $200 million in R&D funding for more than 1,000 customers to date, including Lendesk, Oral4D, and Policy Works. The startup also says it has been doubling growth year-over-year since launch, with annual recurring revenue in the “eight figures” and an over 80% gross margin.
“R&D is the engine of the modern economy, and innovative companies should be able to access the tax credits they’re entitled to,” Popa said in a statement. “We’ve spent almost a decade honing our craft to help customers get more money, faster and with less risk, compared to any other solution in the market. We empower companies from startups to large enterprises to quantify the value of their R&D efforts, then leverage that asset through a successful R&D tax credit application which allows them to grow their business without unnecessary outside capital.”
The credit facility brings Boast.ai’s total raised to over $120 million following a $23 million fundraising round in December.

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Introducing the AI chip leading the world in precision scaling

Digital AI Cores

Self-driving cars, text to speech, artificial intelligence (AI) services and delivery drones — just a few obvious applications of AI. To keep fueling the AI gold rush, we’ve been improving the very heart of AI hardware technology: digital AI cores that power deep learning, the key enabler of artificial intelligence. At IBM Research, we’ve been making strides in adapting to workload complexities of AI systems while streamlining and accelerating performance…

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Locus Robotics raises $150 million to scale its warehouse robotics platform

Locus Robotics

Locus Robotics, a Wilmington, Massachusetts-based warehouse robotics startup, today announced it has raised $150 million in series E funding at a $1 billion post-money valuation. The company says the funding will allow it to accelerate product innovation and global expansion. Locus expects that in the next four years, over a million warehouse robots will be installed and that the number of warehouses using them will grow tenfold.

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AutoML vs HPO vs CASH: what is the difference?

Robot and doctor with telescope

Automated Machine Learning, or AutoML for short, is on the rise. More and more commercial products appear on the market, academic tools, and public, open-source AutoML libraries. As with every new technology that is new, unclear, and nebulously defined, AutoML is misunderstood. On one end, there are grandiose claims that it will send data analysts home, and on the other end, extreme statements that it automates only the trivial part of the analysis. This is because different people give different definitions to AutoML. Let us examine them.

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Sunlight’s AI Hypervisor gains Nvidia GPU support to boost Edge deployments

Edge Computing

Sunlight, the U.K.-based specialist in virtualizing data-intensive applications, announced Nvidia GPU support for its “lightweight” hypervisor designed to boost the performance of edge AI deployments. GPU support for its NexVisor platform would…

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AI in Robotic Process Automation

Robotic Process Automation

With AI and RPA, business organizations can quickly transform their operational procedures, achieve better results, scale their business and attain full automation. However, both technologies require rare technical skills — a growing concern for corporate businesses around the world. There is an increasing need for tech professionals who can successfully leverage them.

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Recogni raises $48.9 million for AI-powered perception chips

Prism Chip

Recogni, a startup designing an AI-powered vision recognition module for autonomous vehicles, today announced it raised $48.9 million. The company says the funds will help it bring its perception product to market while expanding the size of its engineering and go-to-market teams.

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