Cloud Hosting

The most unusual thing about this weeks Senate hearing on AI was how affable it was. Industry reps primarily OpenAI CEO Sam Altman merrily agreed on the need to regulate new AI technologies, while politicians seemed happy to hand over responsibility for drafting rules to the companies themselves. As Senator Dick Durbin (D-IL) put it in his opening remarks: I cant recall when weve had people representing large corporations or private sector entities come before us and plead with us to regulate them.

This sort of chumminess makes people nervous. A number of experts and industry figures say the hearing suggests we may be headed into an era of industry capture in AI. If tech giants are allowed to write the rules governing this technology, they say, it could have a number of harms, from stifling smaller firms to introducing weak regulations.

Industry capture could harm smaller firms and lead to weak regulations

Experts at the hearing included IBMs Christina Montgomery and noted AI critic Gary Marcus, who also raised the specter of regulatory capture. (The peril, said Marcus, is that we make it appear as if we are doing something, but its more like greenwashing and nothing really happens, we just keep out the little players.) And although no one from Microsoft or Google was present, the unofficial spokesperson for the tech industry was Altman.

Although Altmans OpenAI is still called a startup by some, its arguably the most influential AI company in the world. Its launch of image and text generation tools like ChatGPT and deals with Microsoft to remake Bing have sent shockwaves through the entire tech industry. Altman himself is well positioned: able to appeal to both the imaginations of the VC class and hardcore AI boosters with grand promises to build superintelligent AI and, maybe one day, in his own words, capture the light cone of all future value in the universe.

At the hearing this week, he was not so grandiose. Altman, too, mentioned the problem of regulatory capture but was less clear about his thoughts on licensing smaller entities. Wedont wanna slow down smaller startups. We dont wanna slow down open source efforts, he said, adding, We still need them to comply with things.

Sarah Myers West, managing director of the AI Now institute, tells The Verge she was suspicious of the licensing system proposed by many speakers. I think the harm will be that we end up with some sort of superficial checkbox exercise, where companies say yep, were licensed, we know what the harms are and can proceed with business as usual, but dont face any real liability when these systems go wrong, she said.

Requiring a license to train models would ... further concentrate power in the hands of a few

Other critics particularly those running their own AI companies stressed the potential threat to competition. Regulation invariably favours incumbents and can stifle innovation, Emad Mostaque, founder and CEO of Stability AI, told The Verge. Clem Delangue, CEO of AI startup Hugging Face, tweeted a similar reaction: Requiring a license to train models would be like requiring a license to write code. IMO, it would further concentrate power in the hands of a few & drastically slow down progress, fairness & transparency.

But some experts say some form of licensing could be effective. Margaret Mitchell, who was forced out of Google alongside Timnit Gebru after authoring a research paper on the potential harms of AI language models, describes herself as a proponent of some amount of self-regulation, paired with top-down regulation. She told The Verge that she could see the appeal of certification but perhaps for individuals rather than companies.

You could imagine that to train a model (above some thresholds) a developer would need a commercial ML developer license, said Mitchell, who is now chief ethics scientist at Hugging Face. This would be a straightforward way to bring responsible AI into a legal structure.

Mitchell added that good regulation depends on setting standards that firms cant easily bend to their advantage and that this requires a nuanced understanding of the technology being assessed. She gives the example of facial recognition firm Clearview AI, which sold itself to police forces by claiming its algorithms are 100 percent accurate. This sounds reassuring, but experts say the company used skewed tests to produce these figures. Mitchell added that she generally does not trust Big Tech to act in the public interest. Tech companies [have] demonstrated again and again that they do not see respecting people as a part of running a company, she said.

Even if licensing is introduced, it may not have an immediate effect. At the hearing, industry representatives often drew attention to hypothetical future harms and, in the process, gave scant attention to known problems AI already enables.

For example, researchers like Joy Buolamwini have repeatedly identified problems with bias in facial recognition, which remains inaccurate at identifying Black faces and has produced many cases of wrongful arrest in the US. Despite this, AI-driven surveillance was not mentioned at all during the hearing, while facial recognition and its flaws were only alluded to once in passing.

Industry figures often stress future harms of AI to avoid talking about current problems

AI Nows West says this focus on future harms has become a common rhetorical sleight of hand among AI industry figures. These individuals position accountability right out into the future, she said, generally by talking about artificial general intelligence, or AGI: a hypothetical AI system smarter than humans across a range of tasks. Some experts suggest were getting closer to creating such systems, but this conclusion is strongly contested.

This rhetorical feint was obvious at the hearing. Discussing government licensing, OpenAIs Altman quietly suggested that any licenses need only apply to future systems. Where I think the licensing scheme comes in is not for what these models are capable of today, he said. But as we head towards artificial general intelligence thats where I personally think we need such a scheme.

Experts compared Congress (and Altmans) proposals unfavorably to the EUs forthcoming AI Act. The current draft of this legislation does not include mechanisms comparable to licensing, but it does classify AI systems based on their level of risk and imposes varying requirements for safeguards and data protection. More notable, though, is its clear prohibitions of known and current harmful AI uses cases, like predictive policing algorithms and mass surveillance, which have attracted praise from digital rights experts.

As West says, Thats where the conversation needs to be headed if were going for any type of meaningful accountability in this industry.

See the article here:

The Senate's hearing on AI regulation was dangerously friendly - The Verge

The advent of artificial intelligence (AI) seems to have sent shockwaves through the world. Not a day passes without my clients and students contemplating its effects. James, a young journalist, told me, Maybe AIs will be wonderful assistants when I do research, while Ravi, an electrical engineering student, is less optimistic. In five years, our society will be completely changed by the influence of AI. He adds, The problem is that AI will develop in ways we cant foresee, with exponential speed. He looks downcast. I find that deeply depressing, and it leaves me sleepless at night.

AI is a widespread source of anxiety. Many worry that their jobs will be obsolete since AI can do some tasks faster and better than humans. Tristan Harris, from the Centre of Humane Technology, warns that privacy can easily be violated in many areas of our lives. He also suggests that AI systems can aggravate existing societal biases and discriminations. Malicious actors can exploit vulnerabilities in AI systems, leading to the manipulation of public opinion. This could lead to negative psychological impacts on marginalized individuals or communities. He is especially concerned about the potential for AI to increase polarization. Intelligent algorithms could radicalize moderate consumers to capture attention.

Juan, a young scientist from our local university interested in existential and spiritual concerns, has an exciting view. I wish we could program a longing for wisdom and goodness into our AI. Then AI could influence our society in a positive, compassionate way.

Leike, Schulman, and Wu speak about the AI alignment problem, which describes the degree of correspondence between the values and motivations of humans and AI. They tell us, Unaligned AGI could pose significant risks to humanity, and solving the AGI (Artificial General Intelligence) alignment problem could be so difficult that it will require all humanity to work together.

Philosopher Nick Bostrom notes that to build an AI that acts safely while acting in many domains, with many consequences, including problems engineers never explicitly envisioned, one must specify good behavior in as in X equals such that the consequence of X is not harmful to humans. In brief, many AI researchers and thinkers believe good, human-compatible intentions must be explicitly programmed into how AI systems are designed. AI systems carry the intentions of the people who create them, whether coded intentionally or not.

AI reflects the intention of the person who created it. If the intention is to make a profit, then that is what the AI will do. If the AI intends to replace a humans work, then that is what the AI will do. If the intention is to mimic a humans voice and expression, then that is what the AI will do. AI has no inherent sense of caring, intuition, or intrinsic conscience.

To tackle the enormous problem of how to ethically and safely achieve the goal of integrating AI into society, we need to build alignment and intention. Alignment refers to the state of congruence between values, beliefs, and goals. In terms of purpose, it involves aligning our actions and plans with a more profound sense of purpose in life. Intentions play a crucial role in shaping our experiences and outcomes and help us to stay focused, motivated, and in tune with our goals, even when we face challenges. By being fully present with our inner values, we align our intentions with our core values. When alignment and intention work, they create a powerful positive synergy.

That is why we have to clarify our intention, tune into our intuition, and be conscious when programming AI. We need the utmost clarity and self-awareness as an individual, a group of scientists, a society, or an international decision-making body.

Buddhist psychology can help us here. It emphasizes the importance of cultivating wholesome intentions. Wholesome intention originates in our generosity, loving kindness, compassion, and the absence of harmful desires. This leads indirectly to positive outcomes by promoting behaviors aligned with those intentions. Meditation and mindfulness allow individuals to make conscious choices that lead to well-being and spiritual progress.

It is important to note that Buddhist psychology does not solely focus on intentions but also considers the actual consequences of actions. Intention alone is insufficient to determine an effort's ethical value, as the outcome and impact on oneself and others are also considered. However, intention serves as a crucial starting point and a significant factor in determining the ethical quality of an action.

I recommend that we lean on positive and Buddhist psychology to develop a playbook for making AI useful for individuals, groups, and societies so we successfully support our health and well-being. The potential threats of unaligned AI are undoubtedly immense, but we are not powerless in their midst. Careful alignment and encoding of positive human values in AI systems require us to understand our values, intentions, and motivations. Here, Buddhist psychology can serve as a guide, offering practices to listen, discover our intentions, and more deeply align them with our best interests.

See more here:

Programmed Values: The Role of Intention in Developing AI - Psychology Today

The artificial intelligence (AI) boom has brought with it a cornucopia of jargon from "generative AI" to "synthetic data" that can be hard to parse. And as hard as it is to really understand what AI is (see our explainer for that) having a working knowledge of AI terms can help make sense of this technology.

As part of our series explaining the basics of AI, here is a short glossary of terms that will hopefully help you navigate the rapidly developing field.

Artificial Intelligence: Technology that aims to replicate human-like thinking within machines. Some examples of abilities that fall into this category include identifying people in pictures, work in factories and even doing taxes.

Generative AI: Generative AI is an AI that can create things like text, images, sound and video.Traditional applications of AI largely classify content, while generative AI models create it. For instance, a voice recognition model can identify your voice, while a generative voice model can use your voice to create audiobooks. Almost all models that have recently captured the publics attention have been generative, including chatbots like OpenAIs ChatGPT, image creators like Stable Diffusion and MidJourney, and voice-cloning programs like Resemble.

Training Data: A collection of information text, image, sound curated to help AI models accomplish tasks. In language models, training datasets focus on text-based materials like books, comments from social media, and even code. Because AI models learn from training data, ethical questions have been raised around its sourcing and curation. Low-quality training data can introduce bias, leading to unfair models that make racist or sexist decisions.

Algorithmic Bias: An error resulting from bad training data and poor programming that causes models to make prejudiced decisions. Such models may draw inappropriate assumptions based on gender, ability or race. In practice, these errors can cause serious harm by affecting decision-making from mortgage applications to organ-transplant approvals. Many critics of the speedy rollout of AI have invoked the potential for algorithmic bias.

Artificial General Intelligence (AGI): A description of programs that are as capable or even more capable than a human. While full general intelligence is still off in the future, models are growing in sophistication. Some have demonstrated skills across multiple domains ranging from chemistry to psychology, with task performance paralleling human benchmarks.

Autonomous Agents: An AI model that has both an objective and enough tools to achieve it. For instance, self-driving cars are autonomous agents that use sensory input, GPS data and driving algorithms to make independent decisions about how to navigate and reach destinations. A group of autonomous agents can even develop cultures, traditions and shared language, as researchers from Stanford have demonstrated.

Prompt Chaining: The process of using previous interactions with an AI model to create new, more finely tuned responses, specifically in prompt-driven language modeling. For example, when you ask ChatGPT to send your friend a text, you expect it to remember things like the tone you use to talk to her, inside jokes and other content from previous conversations. These techniques help incorporate this context.

Large Language Models (LLM): An application of AI usually generative that aims to understand, engage and communicate with language in a human-like way. These models are distinguished by their large size: The biggest version of GPT-3, a direct predecessor to ChatGPT, contained 175 billion different variables called parameters that were trained on 570 gigabytes of data. Googles PaLm model is even larger, having 540 billion parameters. As hardware and software continue to advance, this scale is expected to increase.

Hallucination: Hallucinations are unexpected and incorrect responses from AI programs that can arise for reasons that are not yet fully known. A language model might suddenly bring up fruit salad recipes when you were asking about planting fruit trees. It might also make up scholarly citations, lie about data you ask it to analyze, or make up facts about events that arent in its training data. Its not fully understood why this happens, but can arise from sparse data, information gaps and misclassification.

Emergent Behavior: Skills that AI might demonstrate that it was not explicitly built for. Some examples include emoji interpretation, sarcasm and using gender-inclusive language. A research team at Google Brain identified over 100 of these behaviors, noting that more are likely to emerge as models continue to scale.

Alignment: Efforts to ensure AI systems share the same values and goals as their human operators. To bring motives into agreement, alignment research seeks to train and calibrate models, often using functions to reward or penalize models. If the model does a good job, you give it positive feedback. If not, you give it negative feedback.

Multimodal AI: A form of AI that can understand and work with multiple types of information, including text, image, speech and more. This is powerful because it allows AI to understand and express itself in multiple dimensions, giving both a broader and more nuanced understanding of tasks. One application of multimodal AI is this translator, which can convert Japanese comics into English.

Prompt Engineering: This is the act of giving AI an instruction so it has the context it needs to achieve your goal. Prompt engineering is best associated with OpenAIs ChatGPT, describing the tasks users feed into the algorithm (e.g. Give me five popular baby names).

Training: Training is the process of refining AI using data so its better suited for a task. An AI can be trained by feeding in data based on what you want it to learn from like feeding Shakespearean sonnets to a poetry bot. You can do this multiple times in iterations called epochs, until your models performance is consistent and reliable.

Neural Networks: Neural networks are computer systems constructed to approximate the structure of human thought, specifically via the structure of your brain. Theyre built like thisbecause they allow a model to build up from the abstract to the concrete. In an image model, initial layers, concepts like color or position might be formed, building up to firmer, more familiar forms like fruit or animals.

Narrow AI: Some AI algorithms have a one-track mind. Literally. Theyre designed to do one thing and nothing more. If a narrow AI algorithm can play checkers, it cant play chess. Examples include algorithms that only detect NSFW images and recommendation engines designed to tell you what Amazon or Etsy product to buy next.

Jasmine Cui is a reporter for NBC News.

Jason Abbruzzese is the senior editor for technology, science and climate for NBC News Digital.

More here:

AI glossary: words and terms to know about the booming industry - NBC News

Artificial intelligence (AI)

School leaders announce launch of body to protect students from the risks of artificial intelligence

Sat 20 May 2023 05.50 EDT

Schools are bewildered by the fast pace of development in artificial intelligence and do not trust tech firms to protect the interests of students and educational establishments, headteachers have written.

A group of UK school leaders have announced the launch of a body to advise and protect schools from the risks of AI, with their fears not limited to the capacity of chatbots such as ChatGPT to aid cheating. There are also concerns about the impact on childrens mental and physical health as well as the teaching profession itself, according to the Times.

The headteachers fears were outlined in a letter to the Times in which they warned of the very real and present hazards and dangers being presented by AI, which has gripped the public imagination in recent months through the emergence of breakthroughs in generative AI where tools can produce plausible text, images and even voice impersonations on command.

The group of school leaders is led by Sir Anthony Seldon, the head of Epsom College, a fee-paying school, while the AI body is supported by the heads of dozens of private and state schools.

The letter to the Times says: Schools are bewildered by the very fast rate of change in AI and seek secure guidance on the best way forward, but whose advice can we trust? We have no confidence that the large digital companies will be capable of regulating themselves in the interests of students, staff and schools and in the past the government has not shown itself capable or willing to do so.

Signatories to the letter include Seldon, Chris Goodall, the deputy head of Epsom & Ewell High School, and Geoff Barton, general secretary of the Association of School and College Leaders.

It adds that the group is pleased the government is now grasping the nettle on the issue. This week Rishi Sunak said guardrails would have to be put around AI as Downing Street indicated support for a global framework for regulating the technology. However, the letter adds that educational leaders are forming their own advisory body because AI is moving too quickly for politicians to cope.

AI is moving far too quickly for the government or parliament alone to provide the real-time advice schools need. We are thus announcing today our own cross-sector body composed of leading teachers in our schools, guided by a panel of independent digital and AI experts.

Supporters include James Dahl, the head of Wellington College in Berkshire, and Alex Russell, chief executive of the Bourne Education Trust, which runs about two dozen state schools.

The Times reported that the group would create a website led by the heads of science or digital at 15 state and private schools, offering guidance on developments in AI and what technology to avoid or embrace.

Seldon told the Times: Learning is at its best, human beings are at their best, when they are challenged and overcome those challenges. AI will make life easy and strip away learning and teaching unless we get ahead of it.

The Department for Education said: The education secretary has been clear about the governments appetite to pursue the opportunities and manage the risks that exist in this space, and we have already published information to help schools do this. We continue to work with experts, including in education, to share and identify best practice.

{{topLeft}}

{{bottomLeft}}

{{topRight}}

{{bottomRight}}

{{.}}

Read the original post:

UK schools bewildered by AI and do not trust tech firms, headteachers say - The Guardian

Zoomhas become the latest tech company riding this years wave of artificial intelligence (AI) integrations.

The video conferencing platform announced in a Tuesday (May 16) press release that it has teamed with and isinvestingin AI firmAnthropic.

The collaboration will integrate Anthropics AI assistant, Claude, with Zooms platform, beginning withZoom Contact Center.

With Claude guiding agents toward trustworthy resolutions and powering self-service for end-users, companies will be able to take customer relationships to another level, saidSmita Hashim, chief product officer for Zoom, in the release.

Working with Anthropic, Hashim said, furthers the companys goal of a federated approach to AI while also advancing leading-edge companies like Anthropic and helping to drive innovation in the Zoom ecosystem and beyond.

As the next step in evolving the Zoom Contact Center portfolio, (Zoom Virtual Agent, Zoom Contact Center, Zoom Workforce Management), Zoom plans to incorporate Anthropic AI throughout its suite, improving end-user outcomes and enabling superior agent experiences, the news release said.

Zoom said in the release it eventually plans to incorporate Anthropic AI throughout its suite, including products like Team Chat, Meetings, Phone, Whiteboard and Zoom IQ.

Last year, Zoom debutedZoom Virtual Agent, an intelligent conversational AI and chatbot tool that employs natural language processing and machine learning to understand and solve customer issues.

The company did not reveal the amount of its investment in Anthropic, which isbackedbyGoogleto the tune of $300 million.

Zooms announcement came amid a flurry of AI-related news Tuesday, with fraud prevention firmComplyAdvantagelaunching anAI tooland the New York Times digging intoMicrosofts claims that it had made a breakthrough in the realm of artificial general intelligence.

Perhaps the biggest news isOpenAICEO Sam Altmanstestimonybefore a U.S. Senate subcommittee, in which he warned: I think if this technology goes wrong, it can go quite wrong.

Altmans testimony happened as regulators and governments around the world step up their examination of AI in a race tomitigate fearsabout its transformative powers, which have spread in step with the future-fit technologys ongoing integration into the broader business landscape.

Go here to read the rest:

Zoom Invests in and Partners With Anthropic to Improve Its AI ... - PYMNTS.com