One-third of organizations are using AI, IBM survey finds

One-third of organizations are using AI, IBM survey finds

1/3 of plan to invest in AI skills over next 12 month, 43% rollout accelerated as a result of the pandemic with customer service is top use case, NLP at the forefront, 1/4 will use AI tech in 2021, and 90% want ability to explain how AI arrived at a decision is important. AI systems will reach $77.6 B in 2022

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Facebook AI conducts large-scale study on Unsupervised Spatiotemporal Representation Learning

A research team from Facebook AI recently published a large-scale study on unsupervised spatiotemporal representation learning from videos, aiming to compare the various meta-methodologies on common ground. With a unified perspective on four current image-based frameworks (MoCo, SimCLR, BYOL, SwAV), the team identifies a simple objective they say can easily generalize all these methodologies to space-time.

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Red Hat open-sources TrustyAI, an auditing tool for AI decision systems

TrustyAI Kogito

Red Hat developed the TrustyAI Explainability Toolkit, a library leveraging techniques for explaining automated decision-making systems. Part of Kogito, Red Hat’s cloud-native business automation framework, TrustyAI enriches AI model execution information through algorithms while extracting, collecting, and publishing metadata for auditing and compliance.

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MIT study finds ‘systematic’ labeling errors in popular AI benchmark datasets

The field of AI and machine learning is arguably built on the shoulders of a few hundred papers, many of which draw conclusions using data from a subset of public datasets. Large, labeled corpora have been critical to the success of AI in domains ranging from image classification to audio classification. That’s because their annotations expose comprehensible patterns to machine learning algorithms, in effect telling machines what to look for in future datasets so they’re able to make predictions.

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Model Scaling that’s both accurate and fast: Facebook AI…

The past several years have seen the rapid development of new hardware for training and running convolutional neural networks. Highly-parallel hardware accelerators such as GPUs and TPUs have enabled machine learning researchers to design and train more complex and accurate neural networks that can be employed in more complex real-life applications.

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[Paper] Top-10 research papers in AI

The most cited AI works that influence our daily life today. Each year scientists from around the world publish thousands of research papers in AI but only a few of them reach wide audiences and make a global impact in the world. Below are the top-10 most impactful research papers published in top AI conferences during the last 5 years. The ranking is based on the number of citations and includes major AI conferences and journals.

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Facebook & Google’s LazyTensor enables expressive domain-specific Compilers

A team from Facebook and Google has proposed LazyTensor — a technique for targeting domain-specific compilers without sacrificing define-by-run ergonomics.
The post Facebook & Google’s LazyTensor Enables Expressive Domain-Specific Compilers first appeared on Synced.

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KoBold Metals – Startup Backed By Bezos, Gates Fund Is Using AI to Find Metals for Electric Vehicles

Metals and battery lab

On Tuesday, KoBold Metals, a mining exploration startup based out of the San Francisco Bay Area, announced a partnership with Stanford University to develop technology aimed at improving the efficiency of mineral mining. The collaboration hopes to help meet the growing demand for metals… necessary for the production of electric vehicle batteries. “Battery metals are to the renewable energy and electric vehicle revolutions what iron was to the Iron Age and what coal was to the Industrial Revolution… the world will need $5 trillion worth of copper, nickel, cobalt, and lithium beyond known reserves.

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Google is changing its paper review process following internal revolt

Google

Google is making changes to how it reviews papers following an internal revolt over the company’s controversial practices. Leading AI ethics researcher Timnit Gebru was fired from Google in December last year after sending an email to colleagues which criticised the company’s practices. Gebru claims Google blocks the publication of papers that may cause criticism […]

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British intelligence agency GCHQ publishes ‘Ethics of AI’ report

GCHQ AI ethics report

The intelligence agency’s first-ever public report details how AI can be used “ethically” for cyber operations. GCHQ (Government Communications Headquarters) is tasked with providing signals intelligence and information assurance to the government and armed forces of the United Kingdom and its allies. Jeremy Fleming, Director of GCHQ, said: “We need honest, mature conversations about the […]

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How Machine Learning and Data Science can advance Nutrition Research

Egg Salad

In this special guest feature, Kyle Dardashti, CEO & Founder of Heali, discusses how machine learning and data science bring exciting potential to the world of personalized nutrition. Heali is a personalized nutrition company focused on supporting people with medical nutrition therapy. Formerly Kyle led a marketing agency and product incubator which he co-founded. He was responsible for innovating global brands with new-age marketing strategies. Today at Heali he leads a diversified and experienced staff combining world-renowned leaders from technology, nutrition and medicine sectors.

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The state of Federal Funding towards AI Research

Funding

AI research has also accelerated in the last 5 years with more research papers published in 2020… Funding of AI research has also accelerated with Congress approving AI related initiatives such as boosting funding in institutions of higher learning. The Biden administration² is currently working on an AI policy that will see the United States continue investing more in AI research, military applications and national security. The current AI funding¹ by the federal government is a good start but more needs to be done.

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Uncovering unknown unknowns in Machine Learning

The performance of machine learning (ML) models depends both on the learning algorithms, as well as the data used for training and evaluation. The role of the algorithms is well studied and the focus of a multitude of challenges, such as SQuAD, GLUE, ImageNet, and many others. In addition, there have been efforts to also improve the data, including a series of workshops addressing issues for ML evaluation. In contrast, research and challenges that focus on the data used for evaluation of ML models are not commonplace.

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U.S.-India Artificial Intelligence Cooperation

US India Summit

The Biden administration plans to prioritize federal funding for U.S. research and development on artificial intelligence (AI) and other advanced technologies. If products created from these technologies are to enter the global marketplace, administration officials will need to work with like-minded countries to create new AI standards and principles consistent with democratic values and fair markets. India is well-placed to be an essential part of these efforts…

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Researchers find systems to counter deepfakes can be deceived

Barak Obama

Researchers have found that systems designed to counter the increasing prevalence of deepfakes can be deceived. The researchers, from the University of California – San Diego, first presented their findings at the WACV 2021 conference. Shehzeen Hussain, a UC San Diego computer engineering PhD student and co-author on the paper, said: “Our work shows that…

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Microsoft details Speller100, an AI system that checks spelling in over 100 languages

Bing

In a post on its AI research blog, Microsoft today detailed a new language system, Speller100, that the company claims is one of the most comprehensive ever made in terms of language coverage and accuracy. Comprising a number of AI models that can understand speech in over 100 languages collectively, Speller100 now powers all spelling correction on Bing.

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The AI industry is built on geographic and social inequality, research shows

The arm of global inequality is long, rendering itself visible particularly in the development of AI and machine learning systems. In a recent paper, researchers at Cornell, the Universite de Montreal, the National Institute of Statistical Sciences (U.S.), and Princeton argue that this inequality in the AI industry involves a concentration of profits and raises the danger of ignoring the contexts to which AI is applied.

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AAAI 2021 best Papers announced

Best Papers

The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) kicked off today as a virtual conference. The organizing committee announced the Best Paper Awards and Runners Up during this morning’s opening ceremony. Three papers received Best Paper Awards and three were recognized as Runners Up. The total of 9,034 submissions to AAAI 2021 marked another record high, surpassing last year’s 8800. Submissions from China (3,319) almost doubled the number of papers from the United States (1,822). Out of 7,911 papers that went to review, a total of 1,692 papers made it. This year’s acceptance rate was 21 percent, slightly higher than last year’s 20.6 percent.

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