Americans generate more than 290 million tons of municipal solid waste each year thats all the packaging, clothing, bottles, food scraps, newspapers, batteries and other everyday items that are thrown into garbage cans.

Some of that waste is recycled, composted or burned for energy, but nearly 50% of it is sent to a landfill where it slowly decomposes and emits greenhouse gases that account for about 25% of todays global warming.

With support from the U.S. Department of Energy, NCState researcher Lokendra Pal has partnered with the National Renewable Energy Laboratory, IBM and the Town of Cary to solve that problem.

Pal, the EJ Woody Rice Professor in the Department of Forest Biomaterials, is working with his collaborators to develop a smart waste management system for the collection, identification and characterization of organic materials in non-recyclable waste.

Non-recyclable waste includes items that are too contaminated for recycling, often because they contain organic materials such as oil, grease and dirt. The researchers want to convert these materials into renewable products, energy and fuel.

The sustainable utilization of non-recyclable waste will empower businesses to utilize it as a renewable carbon resource and will support them in the journey toward a low-carbon economy, Pal said.

In developing the smart waste management system, Pal and his collaborators are integrating smart sensors, visual cameras and hyperspectral cameras with an automated waste sorting machine to examine non-recyclable waste items.

The visual and hyperspectral cameras will capture images of the items as they move along a conveyor belt, while the sensors will help to monitor and control the waste management process.

Most objects absorb and reflect light. Digital cameras can only visualize three color bands of light red, green and blue. Hyperspectral cameras, however, can visualize many more bands from across the electromagnetic spectrum, resulting in images that showcase chemical characteristics that would otherwise be invisible.

By combining hyperspectral imaging with visual cameras and smart sensors, we can collect data in real-time to improve the process of characterizing and separating waste so that it doesnt end up in landfills, Pal said.

Pal and his collaborators are also analyzing non-recyclable waste items to determine their physical, chemical, thermal and biological properties, including moisture, density, particle size and distribution, surface area, crystallinity, calorific value and more. This information will help the system to further differentiate items as theyre scanned.

The researchers plan to upload this metadata information, along with the images and descriptions of the items, to a cloud database to train and test machine learning models that can be integrated with the systems cameras to improve the recognition and classification of non-recyclable waste.

A machine learning model is a type of artificial intelligence that analyzes data to identify patterns, make decisions and improve themselves. In the case of Pals research, the models will analyze the uploaded images and descriptions of non-recyclable waste and the information about its physical, chemical and biological properties to determine contaminants, energy density and organic content.

If successful, this project will contribute significantly to the development of commercially viable, high-performance renewable carbon resources for conversion to biofuels and valued-added products, Pal said.

Pal and his collaborators are exploring the use of various processes andtechnologies to produce fuels such as bioethanol and aviation fuel, which can be blended and used as sustainable fuel in the transportation industry, and products such as biochar, which can be used in agriculture to enhance soil fertility and improve plant growth.

Going forward, the researchers plan to evaluate the technical feasibility and environmental performance of their proposed system at pilot-scale. They also plan to develop a web platform that will enable them to share datasets and other information with stakeholders.

In sum, our approach supports the development of sustainable solutions for waste valorization, optimizing resource recovery, minimizing waste generation, reducing emissions, and mitigating environmental impacts while engaging municipalities and industries across the supply chain, Pal said.

This workshop will explore the solutions, challenges and opportunities ofrecovering organic materials from municipal solid waste for conversion to biofuels, biopower, biochemicals, and bioproducts.

Learn more

Read this article:

AI-Powered Waste Management System to Revolutionize Recycling - NC State College of Natural Resources News

The launch of Elon Musks new Grok artificial intelligence (AI) system may not have yet made waves throughout the machine learning community or directly threatened the status quo, but its certainly drawn the attention of Sam Altman, the CEO of ChatGPT maker OpenAI.

In a post on the social media platform X, formerly Twitter, Altman compared Groks comedic chops to that of a grandpa, saying that it creates jokes similar to your dads dad.

In classic form, Musk apparently couldnt resist the challenge. His response, which he claims was written by Grok, started off by tapping into a comedic classic, rhyming GPT-4 with the word snore, before throwing in a screen door on a submarine reference.

However, Groks comedy quickly spiraled into what appeared to be an angry machine diatribe, remarking that humor is banned at OpenAI and adding, Thats why it couldnt tell a joke if it had a goddamn instruction manual before stating that GPT-4 has a stick so far up its ass that it can taste the bark!

Related: Elon Musk launches AI chatbot Grok, says it can outperform ChatGPT

As far as CEO vs. CEO squabbles go, this one may lack the classic nuance and grace of the legendary Silicon Valley battles of yesteryear (Bill Gates vs. Steve Jobs, for example). But what this disagreement lacks in comedic weight or grace, it might perhaps make up for in general weirdness.

Altman and Musk go way back. Both were co-founders at OpenAI before Musk left the company just in time to avoid getting swept up in the rocket-like momentum thats carried it to a $2 billion valuation.

In the wake of OpenAIs success, which has largely been attributed to the efficacy of its GPT-3 and GPT-4 large language models (LLMs), Musk joined a chorus of voices calling for a six-month pause in AI development,primarily prompted by fears surrounding the supposed potential for chatbots to cause the extinction of the human species.

Six months later, nearly to the day, Musk and X unveileda chatbot model that he claims outperforms ChatGPT.

Dubbed Grok, Musks version of a better chatbot is an LLM supposedly fine-tuned to generate humorous texts in the vein of The Hitchhikers Guide to the Galaxy, a celebrated science fiction novel written by Douglas Adams.

Adams literary work is widely regarded as foundational in the pantheon of comedic science fiction and fantasy. His humor has been described by pundits and literary critics as clever, witty, and full of both heart and humanity.

And that brings us to GPT-4, OpenAIs recently launched GPTs featureallowing users to define a personality for their ChatGPT interface, and Musks insistence that Grok is funnier.

Its currently unclear which model is more robust or capable. There are no standard, accepted benchmarks for LLMs (or comedy, for that matter).

While OpenAI has published several research papers detailing ChatGPTs abilities, X hasnt so far not offered such details about Grok beyond claiming that it outscores GPT-3.5 (an outdated model of the LLM powering ChatGPT) on certain metrics.

Link:

Elon throws AI-generated insults at GPT-4 after OpenAI CEO mocks ... - Cointelegraph

On Oct. 30, 2023, the White House released a long-awaited executive order on artificial intelligence, which covers a wide variety of topics. Here I'll briefly cover the EO and spend more time on the industry responses, which have been numerous.

The EO itself can be found at the Whitehouse.gov briefing room: White House tackles artificial intelligence with new executive order. Heres an opening excerpt:

With this Executive Order, the President directs the most sweeping actions ever taken to protect Americans from the potential risks of AI systems:

A memo from AI.gov covers federal government agency responsibilities and drills down on how agencies will be on the hook for tapping chief AI officers, adding risk management practices to AI and more.

Experts say its emphasis on content labeling, watermarking and transparency represents important steps forward.

What are the new rules around labeling AI-generated content?The White Houses executive order requires the Department of Commerce to develop guidance for labeling AI-generated content. AI companies will use this guidance to develop labeling and watermarking tools that the White House hopes federal agencies will adopt.

Will this executive order have teeth? Is it enforceable?While Bidens executive order goes beyond previous US government attempts to regulate AI, it places far more emphasis on establishing best practices and standards than on how, or even whether, the new directives will be enforced.

What has the reaction to the order been so far? Major tech companies have largely welcomed the executive order.

Brad Smith, the vice chair and president of Microsoft, hailed it as 'another critical step forward in the governance of AI technology.' Googles president of global affairs, Kent Walker, said the company looks 'forward to engaging constructively with government agencies to maximize AIs potentialincluding by making government services better, faster, and more secure.'

EY offers this excellent piece on key takeaways from the Biden administration executive order on AI:

The Executive Order is guided by eight principles and priorities:

On Wednesday and Thursday, delegates from 27 governments around the world, as well as the heads of top artificial intelligence companies, gathered for the worlds first AI Safety Summit at this former stately home near London, now a museum. Among the attendees: representatives of the U.S. and Chinese governments, Elon Musk, and OpenAI CEO Sam Altman.

The high-profile event, hosted by the Rishi Sunak-led U.K. government, caps a year of intense escalation in global discussions about AI safety, following the launch of ChatGPT nearly a year ago. The chatbot displayed for the first timeto many users at leastthe powerful general capabilities of the latest generation of AI systems. Its viral appeal breathed life into a formerly-niche school of thought that AI could, sooner or later, pose an existential risk to humanity, and prompted policymakers around the world to weigh whether, and how, to regulate the technology. Those discussions have been taking place amid warnings not only that todays AI tools already present manifold dangersespecially to marginalized communitiesbut also that the next generation of systems could be 10 or 100 times more powerful, not to mention more dangerous.

Reporting on the summit, The Daily Mail (UK) wrote, "Elon Musk warns AI poses 'one of the biggest threats to humanity' at Bletchley Park summit... but Meta's Nick Clegg says the dangers are 'overstated.'"

Speaking in a conversation with U.K. Prime Minister Rishi Sunak, Musk said that AI will have the potential to become the most disruptive force in history.

But one thing is clear: the new AI EO just signed by President Biden will serve as the near-term road map for most AI related research, testing and development in the US.

Read the original post:

Artificial Intelligence Executive Order: Industry Reactions - Government Technology

President Joe Biden walks across the stage to sign an executive order about artificial intelligence in the East Room at the White House in Washington, D.C., Oct. 30, 2023. REUTERS/Leah Millis

Thecomprehensive, even sweeping, set of guidelinesfor artificial intelligence that the White House unveiled in an executive order on Oct. 30, 2023, shows that the U.S. government is attempting to address the risks posed by AI.

As aresearcher of information systems and responsible AI, I believe the executive order represents an important step in building responsibleandtrustworthyAI.

WATCH: Biden signs order establishing standards to manage artificial intelligence risks

The order is only a step, however, and it leaves unresolved the issue of comprehensive data privacy legislation. Without such laws, people are at greater risk ofAI systems revealing sensitive or confidential information.

Technology is typically evaluated forperformance, cost and quality, but often not equity, fairness and transparency. In response, researchers and practitioners of responsible AI have been advocating for:

The National Institute of Standards and Technology (NIST) issued acomprehensive AI risk management frameworkin January 2023 that aims to address many of these issues. The frameworkserves as the foundationfor much of the Biden administration's executive order. The executive order alsoempowers the Department of Commerce, NIST's home in the federal government, to play a key role in implementing the proposed directives.

Researchers of AI ethics have long cautioned thatstronger auditing of AI systemsis needed to avoid giving the appearance of scrutinywithout genuine accountability. As it stands, a recent study looking at public disclosures from companies found that claims of AI ethics practicesoutpace actual AI ethics initiatives. The executive order could help by specifying avenues for enforcing accountability.

READ MORE: Nations pledge to work together to contain 'catastrophic' risks of artificial intelligence

Another important initiative outlined in the executive order is probing for vulnerabilities ofvery large-scale general-purpose AI modelstrained on massive amounts of data, such as the models that power OpenAI's ChatGPT or DALL-E. The order requires companies that build large AI systems with the potential to affect national security, public health or the economyto perform red teamingand report the results to the government. Red teaming is using manual or automated methods to attempt toforce an AI model to produce harmful output for example, make offensive or dangerous statements like advice on how to sell drugs.

Reporting to the government is important given that a recent study foundmost of the companies that make these large-scale AI systems lackingwhen it comes to transparency.

Similarly, the public is at risk of being fooled by AI-generated content. To address this, the executive order directs the Department of Commerce todevelop guidance for labeling AI-generated content. Federal agencies will be required to useAI watermarking technology that marks content as AI-generated to reduce fraud and misinformation though it's not required for the private sector.

The executive order alsorecognizes that AI systems can pose unacceptable risksofharm to civil and human rightsand the well-being of individuals: "Artificial Intelligence systems deployed irresponsibly have reproduced and intensified existing inequities, caused new types of harmful discrimination, and exacerbated online and physical harms."

A key challenge for AI regulation is the absence of comprehensive federal data protection and privacy legislation. The executive order only calls on Congress to adopt privacy legislation, but it does not provide a legislative framework. It remains to be seen how the courts will interpret the executive order's directives in light of existing consumer privacy and data rights statutes.

Without strong data privacy laws in the U.S. as other countries have, the executive order could have minimal effect on getting AI companies to boost data privacy. In general, it's difficult to measure the impact that decision-making AI systems haveon data privacy and freedoms.

It's also worth noting that algorithmic transparency is not a panacea. For example, the European Union's General Data Protection Regulation legislation mandates "meaningful information about the logic involved" in automated decisions. This suggests a right to an explanation of the criteria that algorithms use in their decision-making. The mandate treats the process of algorithmic decision-making as something akin to a recipe book, meaning it assumes that if people understand how algorithmic decision-making works, they can understandhow the system affects them. But knowing how an AI system works doesn't necessarily tell youwhy it made a particular decision.

With algorithmic decision-making becoming pervasive, the White House executive order and theinternational summit on AI safetyhighlight that lawmakers are beginning to understand the importance of AI regulation, even if comprehensive legislation is lacking.

This article is republished from The Conversation. Read the original article.

Left: President Joe Biden walks across the stage to sign an executive order about artificial intelligence in the East Room at the White House in Washington, D.C., Oct. 30, 2023. REUTERS/Leah Millis

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Analysis: How Bidens new executive order tackles AI risks, and where it falls short - PBS NewsHour

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How Companies Are Hiring And Reportedly Firing With AI - Forbes