Generative AI: What’s Now and Next Google Cloud Next

It’s truly a step change in the history of our species that we’re creating tools that have this kind of, you know, agency. The recent acceleration of technical progress in and awareness of generative AI has been nothing short of staggering. To be sure, we don’t yet have the technology to generate hyper-realistic, live-action video from a text input, and the availability of such technology is key to realizing the new platform. Out of the three, Netflix, is most locked into its business model and will likely find it hard to dramatically change. It long resisted an ad-supported model and has only recently moved in that direction.

You know, instead of just clicking on buttons and typing, you’re going to talk to your AI. [Character is a chatbot for which users can craft different “personalities” and share them online for others to chat with.] It’s mostly used for romantic role-play, and we just said from the beginning that was off the table—we won’t do it. If you try to say “Hey, darling” or “Hey, cutie” or something to Pi, it will immediately push back on you. So two years ago, the conversation—wrongly, I thought at the time—was “Oh, they’re just going to produce toxic, regurgitated, biased, racist screeds.” I was like, this is a snapshot in time. I think that what people lose sight of is the progression year after year, and the trajectory of that progression. I think it’s possible to build AIs that truly reflect our best collective selves and will ultimately make better trade-offs, more consistently and more fairly, on our behalf.

What’s the future of generative AI? An early view in 15 charts

Others deride our methods and our art as based on ‘stealing’ and ‘data theft.’ …many individual artists are afraid of backlash if they so much as touch these important new tools. The full letter and list of signatories is here; the gist is that AI, machine learning and algorithmic or automated tools have been used in music, art and other media for decades and this is just another tool. Another website has  more than two million photos, royalty free, of people who never existed but look like real people.

Research from Goldman Sachs suggests that gen AI has the potential to automate 26% of work tasks in the arts, design, entertainment, media and sports sectors. Certainly, the downsides are significant, ranging from deepfakes to the spread of misinformation on a global scale. For example, a new report claims that China is using AI-generated images to try to influence U.S. voters. Gen AI is already an excellent editor for written content and is becoming a better writer too, as linguistics experts struggle to differentiate AI-generated content from human writing.

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You can select different parameters to get images that fit the specific criteria, and all this is generated by AI; none of these people even exist. Now the typical use case is the intelligent upscaling of low resolution images to high resolution images using complex AI image generation techniques. Better grammar and spelling is something we use everyday without even thinking about.

Keep a human in the loop; that is, make sure a real human checks any gen AI output before it’s published or used. Previous waves of automation technology mostly affected physical work activities, but gen AI is likely to have the biggest impact on knowledge work—especially activities involving decision making and collaboration. Professionals in fields such as education, law, technology, and the arts are likely to see Yakov Livshits parts of their jobs automated sooner than previously expected. This is because of generative AI’s ability to predict patterns in natural language and use it dynamically. On the how—I mean, like, I’m not going to go into too many details because it’s sensitive. But the bottom line is, we have one of the strongest teams in the world, who have created all the largest language models of the last three or four years.

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Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

One reason why today’s solutions are gaining so much attention is they work with much larger volumes of information (hundreds of billions of words) and larger data models (hundreds of billions of parameters) than previous AI systems. As a result, they possess impressive and unprecedented power and can perform very sophisticated functions, like passing the law bar exam and advanced sommelier test. A new wave of artificial intelligence systems, generative AI solutions, like Open AI’s ChatGPT, have tremendous potential to change how work gets done and even how life is lived. They have the potential to streamline business processes, reduce expenses and boost revenue.

These organizations that achieve significant value from AI are already using gen AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management. These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization. Generative AI is an artificial Yakov Livshits intelligence model that, when trained on massive datasets, can generate text, images, audio, and video by predicting the next word or pixel. The simplest input (called a prompt) to generative AI is a text description. Based on that text description, a generative pre-trained transformer (GPT) can write a paragraph, a text-to-image model such as Stable Diffusion can create a picture, MusicLM can create music, and Imagen Video can create a video.

Data Masking

The bill, called the AI Liability Directive, will add teeth to the EU’s AI Act, which is set to become law around a similar time. The AI Act would require extra checks for “high risk” uses of AI that have the most potential to harm people. This could include AI systems used for policing, recruitment, or health care. Under her leadership, MIT Technology Review has been lauded for its editorial authority, its best-in-class events, and its novel use of independent, original research to support both advertisers and readers.

Generative AI also raises numerous questions about what constitutes original and proprietary content. Since the created text and images are not exactly like any previous content, the providers of these systems argue that they belong to their prompt creators. But they are clearly derivative of the previous text and images used to train the models. Needless to say, these technologies will provide substantial work for intellectual property attorneys in the coming years. To start with, a human must enter a prompt into a generative model in order to have it create content. “Prompt engineer” is likely to become an established profession, at least until the next generation of even smarter AI emerges.

Nearly four in ten respondents reporting AI adoption expect more than 20 percent of their companies’ workforces will be reskilled, whereas 8 percent of respondents say the size of their workforces will decrease by more than 20 percent. In conclusion, we haven’t come even close to figuring out what to do about the toxic elements of language models. We’ve only just started examining the harms around text-to-image AI systems. Late last week, Meta unveiled Make-A-Video, an AI that generates five-second videos from text prompts. McKinsey has found that gen AI could substantially increase labor productivity across the economy. To reap the benefits of this productivity boost, however, workers whose jobs are affected will need to shift to other work activities that allow them to at least match their 2022 productivity levels.

• These new tools, she believes, open up a new frontier in copyright challenges, and she helps to create AI policies for her clients.
• McKinsey has found that gen AI could substantially increase labor productivity across the economy.
• But it also often produces distortions or weird problems – for example, an elephant with a second trunk where its tail should be.

Over the next decade, most executives expect that most use cases for generative AI will emerge within the production stage of the game development process, Bain found. This is a switch from the current trend of generative AI mostly being used during preproduction. The videos are only four seconds long, and the video Yakov Livshits is choppy and blurry if you look closely. The system has a way of merging animals like dogs and cats with inanimate objects like balls and cellphones. But given the right prompt, it produces videos that show where the technology is headed. The technology can reproduce common images, like a cat sleeping on a rug.