Amazon launches generative AI to help sellers write product descriptions

The capabilities of generative AI have already proven valuable in areas such as content creation, software development and medicine, and as the technology continues to evolve, its applications and use cases expand. For professionals and content creators, generative AI tools can help with idea creation, content planning and scheduling, search engine optimization, marketing, audience engagement, research and editing and potentially more. Again, the key proposed advantage is efficiency because generative AI tools can help users reduce the time they spend on certain tasks so they can invest their energy elsewhere.

Embedded into the enterprise digital core, generative AI and foundation models will optimize tasks, augment human capabilities and open up new avenues for growth. In the process, these technologies will create an entirely new language for enterprise reinvention. The large language models (LLMs) and foundation models powering these advances in generative AI are a significant Yakov Livshits turning point. They’ve not only cracked the code on language complexity, enabling machines to learn context, infer intent, and be independently creative, but they can also be quickly fine-tuned for a wide range of different tasks. Someone has already written a program called CLIP Interrogator that analyzes an image and comes up with a prompt to generate more images like it.

What are the benefits and applications of generative AI?

Even perfect security systems with thousands of known threat detection rules are not future proof and the adversaries continue to work on new methods of attacks and will inevitably outsmart these security systems. Data and extracting valuable information from it has become critical for successful business operations and planning. That’s not what AI only has to offer, but let’s start with the most common examples, then we can move on to the main topic – generative AI. In this article, we explore what generative AI is, how it works, pros, cons, applications and the steps to take to leverage it to its full potential. Nearly four out of five (79%) business leaders expect their employees will use generative AI often in their work, with 39% anticipating employees will use generative AI every day. Nearly half of the workers we surveyed (45%) say AI reduces or eliminates boring or tedious tasks, while 41% say AI has changed how they work for the better.

Within the technology’s first few months, McKinsey research found that generative AI (gen AI) features stand to add up to $4.4 trillion to the global economy—annually. Generative AI could eventually be used to produce designs for everything from new buildings to new drugs—think text-to-X. Adobe is already building text-to-image generation into Photoshop; Blender, Photoshop’s open-source cousin, has a Stable Diffusion plug-in. And OpenAI is collaborating with Microsoft on a text-to-image widget for Office. Researchers in the field known as computational creativity describe their work as using computers to produce results that would be considered creative if produced by humans alone. What you get back is a handful of images that fit that prompt (more or less).

Generative AI vs. machine learning

By saving advisors and customer service employees time when it comes to questions about markets, recommendations and internal processes, the assistant frees them to engage more with clients, he said. Morgan Stanley, a top investment bank and wealth management juggernaut, made waves in March when it announced that it had been working on an assistant based on OpenAI’s GPT-4. Competitors including Goldman Sachs and JPMorgan Chase have announced projects based on Yakov Livshits. But Morgan Stanley is the first major Wall Street firm to put a bespoke solution based on GPT-4 in employees’ hands, according to Jeff McMillan, head of analytics, data and innovation at Morgan Stanley wealth management. Unfortunately, despite these and future efforts, fake videos and images seem to be an unavoidable price to pay for the benefits we are expected to get from generative AI in the near future. One is generating (for instance images) while  the second is verifying the results, for instance if the images are natural and look true.

Many implications, ranging from legal, ethical, and political to ecological, social, and economic, have been and will continue to be raised as generative AI continues to be adopted and developed. Artificial intelligence has a surprisingly long history, with the concept of thinking machines traceable back to ancient Greece. Modern AI really kicked off in the 1950s, however, with Alan Turing’s research on machine thinking and his creation of the eponymous Turing test.

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.

What’s behind the sudden hype about generative AI?

Focus on people as much as on technology, ramping up talent investments to address both creating AI and using AI. This means developing technical competencies like AI engineering and enterprise architecture and training people across the organization to work effectively with AI-infused processes. We can also expect a large number of new tasks for people to perform, such as ensuring the accurate and responsible use of generative AI systems. It’s why organizations that invest in training people to work alongside generative AI will have a significant advantage. Large companies like Salesforce Inc (CRM.N) as well as smaller ones like Adept AI Labs are either creating their own competing AI or packaging technology from others to give users new powers through software. Our research found that equipping developers with the tools they need to be their most productive also significantly improved their experience, which in turn could help companies retain their best talent.

And ensuring that those boundaries create provable safety all the way from the actual code to the way it interacts with other AIs—or with humans—to the motivations and incentives of the companies creating the technology. And we should figure out how independent institutions or even governments get direct access to ensure that those boundaries aren’t crossed. 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. Amazing people, in an extremely hardworking environment, with vast amounts of computation. We made safety our number one priority from the outset, and as a result, Pi is not so spicy as other companies’ models.

This has given organizations the ability to more easily and quickly leverage a large amount of unlabeled data to create foundation models. As the name suggests, foundation models can be used as a base for AI systems that can perform multiple tasks. In 2014, advancements such as the variational autoencoder and generative adversarial network produced the first practical deep neural networks capable of learning generative, rather than discriminative, models of complex data such as images.

• This technology allows generative AI to identify patterns in the training data and create new content.
• Worse, sometimes it’s biased (because it’s built on the gender, racial, and myriad other biases of the internet and society more generally) and can be manipulated to enable unethical or criminal activity.
• Going forward, a generative AI agent will have a history of working with each individual employee—and will continually be trained by each one from a preferred pool of information.
• Of course it’s science fiction, but with the latest technology we are getting closer to that goal.
• Techniques include VAEs, long short-term memory, transformers, diffusion models and neural radiance fields.

As you may have noticed above, outputs from generative AI models can be indistinguishable from human-generated content, or they can seem a little uncanny. The results depend on the quality of the model—as we’ve seen, ChatGPT’s outputs so far appear superior to those of its predecessors—and the match between the model and the use case, or input. ChatGPT may be getting all the headlines now, but it’s not the first text-based machine learning model to make a splash. OpenAI’s GPT-3 and Google’s BERT both launched in recent years to some fanfare. But before ChatGPT, which by most accounts works pretty well most of the time (though it’s still being evaluated), AI chatbots didn’t always get the best reviews. GPT-3 is “by turns super impressive and super disappointing,” said New York Times tech reporter Cade Metz in a video where he and food writer Priya Krishna asked GPT-3 to write recipes for a (rather disastrous) Thanksgiving dinner.

Similarly, images are transformed into various visual elements, also expressed as vectors. One caution is that these techniques can also encode the biases, racism, deception and puffery contained in the training data. Generative AI is a type of artificial intelligence that can learn from and mimic large amounts of data to create content such as text, images, music, videos, code, and more, based on inputs or prompts. Some examples of foundation models include LLMs, GANs, VAEs, and Multimodal, which power tools like ChatGPT, DALL-E, and more. ChatGPT draws data from GPT-3 and enables users to generate a story based on a prompt.