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As a teenager, I immersed myself in science fiction. While the visions of many films and novels havent come to pass, Im still amazed by legendary writer Isaac Asimovs ability to imagine a future of artificial intelligence and robotics. Now, amid all the hype around generative AI and other AI tools, its time for us to follow Asimovs lead and write a new set of rules.

Of course, AI rules for the 21st century wont be quite as simple as Asimovs three rules of robotics (popularized in I, Robot). But amid anxiety around the rise of AI tools and a misguided push for a moratorium on advanced AI research, industry can and should be pushing for rules for responsible AI development. Certainly, the past centurys advances in technology have given us plenty of experience in evaluating both the benefits of technological progress and the potential pitfalls.

Technology itself is neutral. Its how we use it and the guardrails we set up around it that dictate its impact. As humans, harnessing the power of fire allowed us to stay warm and extend food storage time. But fire can still be destructive.

Think of how the recent Canadian wildfires threatened lives and property in Canada and damaged U.S. air quality. Nuclear power in the form of atomic bombs killed thousands in Japan during WWII, but nuclear energy lights up much of France and powers U.S. aircraft carriers.

In the case of AI, new tools and platforms can solve big global problems and create valuable knowledge. At a recent meeting of Detroit-area chief information officers, attendees shared how generative AI is already speeding up time-to-market and making their companies more competitive.

Generative AI will help us listen to different animal species. AI will improve our health by supporting drug discovery and disease diagnosis. Similar tools are providing everything from personalized care for elders to better security for our homes. More, AI will improve our productivity, with a new study by McKinsey showing generative AI could boost the global economy by $4.4 trillion annually.

With all this possibility, can such an amazing technology also be bad? Some of the concerns around AI platforms are legitimate. We should be concerned about the risk of deep fakes, political manipulation, and fraud aimed at vulnerable populations, but we can also use AI to recognize, intercept and block harmful cyber intrusions. Both barriers and solutions may be difficult and complex, and we need to work on them.

Some may also be simple; we already see schools experimenting with oral exams to test a students knowledge. Addressing those issues head-on, rather than sticking our heads in the sand with a pause on research that would be impossible to enforce and ripe for exploitation by bad actors, will position the United States as a leader on the world stage.

While the U.S. approach to AI has been mixed, other countries seem locked in to a hyper-regulatory stampede. The EU is on the precipice of passing a sweeping AI Act that would require companies to ask permission to innovate. In practice, that would mean that only the government or huge companies with the finances and capacity to afford the certification labyrinth covering privacy, IP, and a host of social protection requirements could develop new AI tools.

A recent study from Stanford University also found that the EUs AI Bill would bar all of the currently existing large language models, including OpenAIs GPT-4 and Googles Bard. Canadian lawmakers are moving forward an overly broad AI bill that could similarly stifle innovation. Most concerning, China is rapidly pursuing civil and military AI dominance through massive government support. More, it has a different view of human rights and privacy protection that may help its AI efforts but is antithetical to our values. The U.S. must act to protect citizens and advance AI innovation or we will be left behind.

What would that look like? To start, the U.S. needs a preemptive federal privacy bill. Todays patchwork of state-by-state rules means that data is treated differently each time it crosses an invisible border causing confusion and compliance hurdles for small businesses. We need a national privacy law with clear guidelines and standards for how companies collect, use, and share data. It would also help create transparency for consumers and ensure that companies can foster trust as the digital economy grows.

We also need a set of principles around responsible AI use. While I prefer less regulation, managing emerging technologies like AI requires clear rules that set out how this technology can be developed and deployed. With new innovations in AI unveiled almost daily, legislators should focus on guardrails and outcomes, rather than attempting to rein in specific technologies.

Rules should also consider the level of risk, focusing on AI systems that could meaningfully hurt Americans fundamental rights or access to critical services. As our government determines what good policy looks like, industry will have a vital role to play. The Consumer Technology Association is working closely with industry and policymakers to develop unified principles for AI use.

Were at a pivotal moment for the future of an amazing, complex and consequential technology. We cant afford to let other countries take the lead.

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Why smart AI regulation is vital for innovation and US leadership - TechCrunch

Large language models (LLMs) have taken the tech industry by storm, powering experiences that can only be described as magicalfrom writing a weeks worth of code in seconds to generating conversations that feel even more empathetic than the ones we have with humans. Trained on trillions of tokens of data with clusters of thousands of GPUs, LLMs demonstrate remarkable natural language understanding and have transformed fields like copy and code, propelling us into the new and exciting generative era of AI. As with any emerging technology, generative AI has been met with some criticism. Though some of this criticism does reflect current limits of LLMs current capabilities, we see these roadblocks not as fundamental flaws in the technology, but as opportunities for further innovation.

To better understand the near-term technological breakthroughs for LLMs and prepare founders and operators for whats around the bend, we spoke to some of the leading generative AI researchers who are actively building and training some of the largest and most cutting edge models: Dario Amodei, CEO of Anthropic; Aidan Gomez, CEO of Cohere; Noam Shazeer, CEO of Character.AI; and Yoav Shoham of AI21 Labs. These conversations identified 4 key innovations on the horizon: steering, memory, arms and legs, and multimodality. In this piece, we discuss how these key innovations will evolve over the next 6 to 12 months and how founders curious about integrating AI into their own businesses might leverage these new advances.

Many founders are understandably wary of implementing LLMs in their products and workflows because of these models potential to hallucinate and reproduce bias. To address these concerns, several of the leading model companies are working on improved steeringa way to place better controls on LLM outputsto focus model outputs and help models better understand and execute on complex user demands. Noam Shazeer draws a parallel between LLMs and children in this regard: its a question of how to direct [the model] better We have this problem with LLMs that we just need the right ways of telling them to do what we want. Small children are like this as wellthey make things up sometimes and dont have a firm grasp of fantasy versus reality. Though there has been notable progress in steerability among the model providers as well as the emergence of tools like Guardrails and LMQL, researchers are continuing to make advancements, which we believe is key to better productizing LLMs among end users.

Improved steering becomes especially important in enterprise companies where the consequences of unpredictable behavior can be costly. Amodei notes that the unpredictability of LLMs freaks people out and, as an API provider, he wants to be able to look a customer in the eye and say no, the model will not do this, or at least does it rarely. By refining LLM outputs, founders can have greater confidence that the models performance will align with customer demands. Improved steering will also pave the way for broader adoption in other industries with higher accuracy and reliability requirements, like advertising, where the stakes of ad placement are high. Amodei also sees use cases ranging from legal use cases, medical use cases, storing financial information and managing financial bets, [to] where you need to preserve the company brand. You dont want the tech you incorporate to be unpredictable or hard to predict or characterize. With better steering, LLMs will also be able to do more complex tasks with less prompt engineering, as they will be able to better understand overall intent.

Advances in LLM steering also have the potential to unlock new possibilities in sensitive consumer applications where users expect tailored and accurate responses. While users might be willing to tolerate less accurate outputs from LLMs when engaging with them for conversational or creative purposes, users want more accurate outputs when using LLMs to assist them in daily tasks, advise them on major decisions, or augment professionals like life coaches, therapists, and doctors. Some have pointed out that LLMs are poised to unseat entrenched consumer applications like search, but we likely need better steering to improve model outputs and build user trust before this becomes a real possibility.

Key unlock: users can better tailor the outputs of LLMs.

Copywriting and ad-generating apps powered by LLMs have already seen great results, leading to quick uptake among marketers, advertisers, and scrappy entrepreneurs. Currently, however, most LLM outputs are relatively generalized, which makes it difficult to leverage them for use cases requiring personalization and contextual understanding. While prompt engineering and fine-tuning can offer some level of personalization, prompt engineering is less scalable and fine-tuning tends to be expensive, since it requires some degree of re-training and often partnering closely with mostly closed source LLMs. Its often not feasible or desirable to fine-tune a model for every individual user.

In-context learning, where the LLM draws from the content your company has produced, your companys specific jargon, and your specific context, is the holy grailcreating outputs that are more refined and tailored to your particular use case. In order to unlock this, LLMs need enhanced memory capabilities. There are two primary components to LLM memory: context windows and retrieval. Context windows are the text that the model can process and use to inform its outputs in addition to the data corpus it was trained on. Retrieval refers to retrieving and referencing relevant information and documents from a body of data outside the models training data corpus (contextual data). Currently, most LLMs have limited context windows and arent able to natively retrieve additional information, and so generate less personalized outputs. With bigger context windows and improved retrieval, however, LLMs can directly offer much more refined outputs tailored to individual use cases.

With expanded context windows in particular, models will be able to process larger amounts of text and better maintain context, including maintaining continuity through a conversation. This will, in turn, significantly enhance models ability to carry out tasks that require a deeper understanding of longer inputs, such as summarizing lengthy articles or generating coherent and contextually accurate responses in extended conversations. Were already seeing significant improvement with context windowsGPT-4 has both an 8k and 32k token context window, up from 4k and 16k token context windows with GPT-3.5 and ChatGPT, and Claude recently expanded its context window to an astounding 100k tokens.

Expanded context windows alone dont sufficiently improve memory, since cost and time of inference scale quasi-linearly, or even quadratically, with the length of the prompt. Retrieval mechanisms augment and refine the LLMs original training corpus with contextual data that is most relevant to the prompt. Because LLMs are trained on one body of information and are typically difficult to update, there are two primary benefits of retrieval according to Shoham: First, it allows you to access information sources you didnt have at training time. Second, it enables you to focus the language model on information you believe is relevant to the task. Vector databases like Pinecone have emerged as the de facto standard for the efficient retrieval of relevant information and serve as the memory layer for LLMs, making it easier for models to search and reference the right data amongst vast amounts of information quickly and accurately.

Together, increased context windows and retrieval will be invaluable for enterprise use cases like navigating large knowledge repositories or complex databases. Companies will be able to better leverage their proprietary data, like internal knowledge, historical customer support tickets, or financial results as inputs to LLMs without fine-tuning. Improving LLMs memory will lead to improved and deeply customized capabilities in areas like training, reporting, internal search, data analysis and business intelligence, and customer support.

In the consumer space, improved context windows and retrieval will enable powerful personalization features that can revolutionize user experiences. Noam Shazeer believes that one of the big unlocks will be developing a model that both has a very high memory capacity to customize for each user but can still be served cost-effectively at scale. You want your therapist to know everything about your life; you want your teacher to understand what you know already; you want a life coach who can advise you about things that are going on. They all need context. Aidan Gomez is similarly excited by this development. By giving the model access to data thats unique to you, like your emails, calendar, or direct messages, he says, the model will know your relationships with different people and how you like to talk to your friends or your colleagues and can help you within that context to be maximally useful.

Key unlock: LLMs will be able to take into account vast amounts of relevant information and offer more personalized, tailored, and useful outputs.

The real power of LLMs lies in enabling natural language to become the conduit for action. LLMs have a sophisticated understanding of common and well-documented systems, but they cant execute on any information they extract from those systems. For example, OpenAIs ChatGPT, Anthropics Claude, and Character AIs Lily can describe, in detail, how to book a flight, but they cant natively book that flight themselves (though advancements like ChatGPTs plugins are starting to push this boundary). Theres a brain that has all this knowledge in theory and is just missing the mapping from names to the button you press, says Amodei. It doesnt take a lot of training to hook those cables together. You have a disembodied brain that knows how to move, but it doesnt have arms or legs attached yet.

Weve seen companies steadily improve LLMs ability to use tools over time. Incumbents like Bing and Google and startups like Perplexity and You.com introduced search APIs. AI21 Labs introduced Jurassic-X, which addressed many of the flaws of standalone LLMs by combining models with a predetermined set of tools, including a calculator, weather API, wiki API, and database. OpenAI betaed plugins that allow ChatGPT to interact with tools like Expedia, OpenTable, Wolfram, Instacart, Speak, a web browser, and a code interpreteran unlock that drew comparisons to Apples App Store moment. And more recently, OpenAI introduced function calling in GPT-3.5 and GPT-4, which allows developers to link GPTs capabilities to whatever external tools they want.

By shifting the paradigm from knowledge excavation to an action orientation, adding arms and legs has the potential to unlock a range of use cases across companies and user types. For consumers, LLMs may soon be able to give you recipe ideas then order the groceries you need, or suggest a brunch spot and book your table. In the enterprise, founders can make their apps easier to use by plugging in LLMs. As Amodei notes, for features that are very hard to use from a UI perspective, we may be able to make complicated things happen by just describing them in natural language. For instance, for apps like Salesforce, LLM integration should allow users to give an update in natural language and have the model automatically make those changessignificantly cutting down the time required to maintain the CRM. Startups like Cohere and Adept are working on integrations into these kinds of complex tools.

Gomez believes that, while its increasingly likely that LLMs will be able to use apps like Excel within 2 years, theres a bunch of refinement that still needs to happen. Well have a first generation of models that can use tools that will be compelling but brittle. Eventually, well get the dream system, where we can give any software to the model with some description of heres what the tool does, heres how you use it, and itll be able to use it. Once we can augment LLMs with specific and general tools, the sort of automation it unlocks is the crown jewel of our field.

Key unlock: LLMs will be able to interact much more effectively with the tools we use today.

While the chat interface is exciting and intuitive for many users, humans hear and speak language as or more often than they write or read it. As Amodei notes, there is a limit to what AI systems can do because not everything is text. Models featuring multimodality, or the ability to seamlessly process and generate content across multiple audio or visual formats, changes this interaction to beyond language. Models like GPT-4, Character.AI, and Metas ImageBind already process and generate images, audio, and other modalities, but they do so at a more basicthough quickly improvinglevel. In Gomezs words, our models are blind in a literal sense todaythat needs to change. Weve built a lot of graphical user interfaces (GUIs) that assume [the user] can see.

As LLMs evolve to better understand and interact with multiple modalities, theyll be able to use existing apps that rely on GUIs today, like the browser. They can also offer more engaging, connected, and comprehensive experiences to consumers, who will be able to engage outside of a chat interface. A lot of great integration with multimodal models can make things a lot more engaging and connected to the user, Shazeer points out. I believe, for now, most of the core intelligence comes from text, but audio and video can make these things more fun. From video chats with AI tutors to iterating and writing TV pilot scripts with an AI partner, multimodality has the potential to change entertainment, learning and development, and content generation across a variety of consumer and enterprise use cases.

Multimodality is also closely tied to tool use. While LLMs might initially connect with outside software through APIs, multimodality will enable LLMs to use tools designed for humans that dont have custom integrations, like legacy ERPs, desktop applications, medical equipment, or manufacturing machinery. Were already seeing exciting developments on this front: Googles Med-PaLM-2 model, for instance, can synthesize mammograms and X-rays. And as we think longer-term, multimodalityparticularly integration with computer visioncan extend LLMs into our own physical reality through robotics, autonomous vehicles, and other applications that require real-time interaction with the physical world.

Key unlock: Multimodal models can reason about images, video, or even physical environments without significant tailoring.

While there are real limitations to LLMs, researchers have made astounding improvements to these models in a short amount of timein fact, weve had to update this article multiple times since we started writing it, a testament to the lightning-fast progression of this technology in the field. Gomez agrees: An LLM making up facts 1 in 20 times is obviously still too high. But I really still feel quite confident that its because this is the first time weve built a system like that. Peoples expectations are quite high, so the goal post has moved from computer is dumb and does only math to a human couldve done this better. Weve sufficiently closed the gap so that criticism is around what a human can do.

Were particularly excited about these 4 innovations, which are on the cusp of changing the way founders build products and run their companies. The potential is even greater in the long term. Amodei predicts that, at some point, we could have a model that will read through all the biological data and say: heres the cure for cancer. Realistically, the best new applications are likely still unknown. At Character.AI, Shazeer lets the users develop those use cases: Well see a lot of new applications unlocked. Its hard for me to say what the applications are. There will be millions of them and the users are better at figuring out what to do with the technology than a few engineers. We cant wait for the transformative effect these advancements will have on the way we live and work as founders and companies are empowered with these new tools and capabilities.

Thanks to Matt Bornstein, Guido Appenzeller,and Rajko Radovanovi for their input and feedback during the writing process.

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The Next Token of Progress: 4 Unlocks on the Generative AI Horizon - Andreessen Horowitz

While attending Colby College in the late 1990s, Ken Allen spent half his time shooting hoops and the other half devoted to day trading. Torn when the time came to search for a job, the economics major first consulted a mentor who connected him with a former student. That connection led the Maine native to T. Rowe Price in Baltimore. Since joining investment management firm in 2000, Allen's worked his way up the corporate ladder, taking the helm as portfolio manager of the T. Rowe Price Science & Technology (PRSCX) fund in 2009. The growth fund launched in 1987 aims to capitalize on innovation. In the wake of the latest artificial intelligence boom that's driven up last year's beaten-up technology stocks, it's skyrocketed nearly 38% in 2023. Longer-term, the fund, with a 0.84% expense ratio, offers 10- and 15-year trailing returns of more than 16% and about 13%, respectively. PRSCX YTD mountain PRSCX shares in 2023 Key to Allen's strategy since he began running the more than $7-billion fund is finding stocks he views as undervalued, hinging his investment framework around an analysis of cash flows. Allen also views solid, independent research as critical pieces of his methodology. This year, Allen has benefited from a spectacular run-up in technology stocks, fueled by a mania for all things tied to artificial intelligence. That said, "I try to stay really disciplined around valuation," Allen maintains. "I think especially in tech investing, valuation tends to get ignored or largely ignored." Betting on FAANG stocks PRSCX owns all the major FAANG names (Meta, Apple, Amazon and Alphabet), except for Netflix, and dominant chipmakers benefiting from AI such as Nvidia and Advanced Micro Devices , up 189% and 70%, respectively, this year. Other key holdings include Microsoft , Salesforce and German online retailer Zalando. Just two stocks combined Microsoft and Alphabet made up nearly a fifth of the fund's portfolio as of as of March 31. Both stocks have gained more than 38% this year as investors bet on the promise of AI and the pair battle to offer the best generative AI model. But the fund's favored Microsoft since 2008 and bought Alphabet last summer, long before their latest gains. Allen expects recent technology trends to continue boosting what he regards as high-quality names, and applies the same philosophy to a position in Amazon now the fund's fourth-largest holding. Allen stood by Microsoft even as a PC-fueled down cycle and uncertainty rocked the technology sector in 2022, driving down shares in the Xbox maker by about 29%. Allen opened his position in Alphabet last year, just as the slowing advertising cycle turned many investors away from the Google parent, dropping its multiple to the low teens. Allen viewed its solid fundamentals as unchanged. "It's kind of unusual to get a great business at a lower PE than it's likely ...growth rate, and that's why I felt really comfortable having a particularly large position then in the stock," he explained. The same idea cemented Allen's faith in Salesforce last year, even as shares cratered 48%, its business slowed and the co-CEO announced his departure. The stock proved one of Allen's biggest losers in 2022, but the portfolio manager expects significant upside as IT spending improves. Salesforce has rebounded more than 58% this year. "I looked at this company that I think is a top line grower, well into the double digits, with a ton of margin expansion, trading at 15 times free cash flow, and it just didn't make sense to me that a business this good would be this discounted," Allen said. Given his devotion to valuation, Allen has lately pulled back his exposure to some technology names that have rallied, significantly reducing stakes in Nvidia, Meta Platforms, Advanced Micro Devices and Amazon. Unusual plays and new additions Not every name in Allen's portfolio is widely bought on Wall Street. Despite a recent selloff in Zalando , Allen said shares look "really cheap" and the company appears well-situated to take market share with its vast item selection. The stock, one of Allen's top 10 as of March 31, is undervalued by at least half on the basis of its long-term cash flow projections, and could potentially triple within the next few years, he calculates, he said. Accenture , which this week forecast weaker-than-estimated revenue in the current quarter , remains a "premier" technology services company able to guide businesses as they look to implement AI. It was Allen's 7th largest position at the end of March. Recent additions to the fund include Mastercard , Apple and Texas Instruments . While Mastercard lags the stock performance of many technology behemoths, Allen believes it offers similar growth potential, and less cyclicality and fewer risks. A low-risk approach also extends to Texas Instruments. While the analog semiconductor name is ahead less than 2% this year, Allen said it offers a strong track record of driving shareholder value and returning capital through buybacks and dividends. TXN yields 3%. Lately, the T. Rowe Price fund has outperformed even when it's slumped. The fund tumbled more than 35% during last year's rout, but other tech funds lost an average of 37.4%, according to Morningstar. For Allen, every investment and every cycle marks another learning opportunity to perfect his craft. "It's really important to learn over time when things go well and especially when things don't go well," Allen said. "One of the things that I focus on a lot, and have for the 23 years I've been doing this, is thoughtfully evaluating what I can do to just get incrementally better while sticking to a process that I believe in."

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Bets on A.I. and innovation help this tech-focused T. Rowe Price ... - CNBC

Technology

This week in artificial intelligence

Fri 23 Jun 2023 08.00 EDT

Artificial intelligence is either going to save humanity or finish it off, depending on who you speak to. Either way, every week there are new developments and breakthroughs. Here are just some of the AI stories that have emerged in recent days

The Wimbledon tennis tournament revealed it will be introducing AI-generated audio and text commentary in its online highlights this year. The All England Club has teamed up with the tech group IBM to provide automatically created voiceovers and captions for its footage. The move, which is separate to the BBCs coverage of the tournament, follows use of the cloned voice of a British athletics commentator, Hannah England, for online coverage of the European Athletics Championships. Generative AI refers to the creation of text and images from a human prompt think ChatGPT and Midjourney but voice is becoming a prominent development in this area as well.

Fears over the existential threat posed by AI have come to the fore in recent months, but the potential impact on jobs is never far behind. A US visual effects company was forced this week to state that the use of AI in the opening sequence of a Disney+ series, Marvels Secret Invasion, did not mean someones job had been displaced.

The film industry has been a locus for AI-related job concerns in recent months, which is understandable given that generative AI has obvious implications for workers and artists in fields such as film, TV and music. Fears over the use of AI in scriptwriting have been a factor in the US screenwriters strike, while the US arts and media union Sag-Aftra is demanding guardrails for replicating actors images and voices in productions.

Another example of how AI could end up affecting journalism was highlighted when Germanys Bild tabloid, the biggest-selling newspaper in Europe, announced a 100m (85m) cost-cutting programme that would lead to about 200 redundancies. It warned staff that it expected to make further editorial cuts owing to the opportunities of artificial intelligence. Bilds publisher, Axel Springer SE, said in an email to staff seen by the rival Frankfurter Allgemeine newspaper that it would unfortunately be parting ways with colleagues who have tasks that in the digital world are performed by AI and/or automated processes.

Advances in AI are exciting, but just as important to the spread of the technology is its productisation: how it gets turned from a promising tech to a real product. Take FabricGenie, from the Millshop Online, a curtain retailer. Enter your design preferences as text, image or sketch, and the company runs a simple AI image generator to spit out unique patterns that you can print on to personalised drapes. Its not going to win any awards for cutting-edge technology, but its the sort of thing that will be more and more common across society over the coming years.

On Thursday a US judge ordered two lawyers and their law firm to pay a $5,000 (4,000) fine after ChatGPT generated fake citations in a legal filing. A district judge in Manhattan ordered lawyers Steven Schwartz, Peter LoDuca and their law firm Levidow, Levidow & Oberman to pay the fine after fictitious legal research was used in an aviation injury claim. Schartz had admitted that ChatGPT, whose responses can appear very plausible, had invented six cases he referred to in a legal brief in a case against the airline Avianca. The legal work sector is a prime candidate for being transformed by generative AI, but this case raises questions over the extent to which AI can replace human work for now.

The UK government is taking warnings about artificial intelligence and safety seriously, before Rishi Sunak hosts a global summit on AI safety in the autumn. Last Sunday it announced that a tech entrepreneur who has warned about an unchecked race to achieve godlike AI will be the head of a new AI advisory body. Ian Hogarth wrote in April that a small number of companies were competing to achieve a breakthrough in computer superintelligence without knowing how to pursue their aim safely and with no oversight. Existential fears about AI include the emergence of a system that evades human intervention, or makes decisions that deviate from human moral values.

Hogarth will now have some influence in moderating the AI arms race as the chair of the UK governments AI Foundation Model taskforce (referring to the underlying technology for AI tools such as text or image generators). Writing in the Times after his appointment was announced, Hogarth said he had saw reasons for more optimism including further calls for action from AI experts and a 100m spending pledge for the UK taskforce, whose role will include identifying and tackling the safety challenges posed by the technology.

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AI watch: from Wimbledon to job losses in journalism - The Guardian