Amazon debuts generative AI tools that helps sellers write product descriptions
In March 2023, Bard was released for public use in the United States and the United Kingdom, with plans to expand to more countries in more languages in the future. It made headlines in February 2023 after it shared incorrect information in a demo video, causing parent company Alphabet (GOOG, GOOGL) shares to plummet around 9% in the days following the announcement. Today professional services leader EY announced the launch of EY.ai, a comprehensive platform Yakov Livshits to help clients boost AI adoption. Sellers will also be able to add to their existing product descriptions using AI, instead of having to start from scratch. XPENG’s inaugural presence at IAA served as the ideal opportunity to introduce its latest models to Europe, including its G9 and P7 EVs, with NVIDIA DRIVE Orin under the hood. Deliveries of the P7 recently commenced, with the vehicles now available in Norway, Sweden, Denmark and the Netherlands.
Further development of neural networks led to their widespread use in AI throughout the 1980s and beyond. In 2014, a type of algorithm called a generative adversarial network (GAN) was created, enabling generative AI applications like images, video, and audio. Google was another early leader in pioneering transformer AI techniques for processing language, proteins and other types of content. Microsoft’s decision to implement GPT into Bing drove Google to rush to market a public-facing chatbot, Google Bard, built on a lightweight version of its LaMDA family of large language models. Google suffered a significant loss in stock price following Bard’s rushed debut after the language model incorrectly said the Webb telescope was the first to discover a planet in a foreign solar system.
On top of that, transformers can run multiple sequences in parallel, which speeds up the training phase. It extracts all features from a sequence, converts them into vectors (e.g., vectors representing the semantics and position of a word in a sentence), and then passes them to the decoder. Some of the most well-known examples of transformers are GPT-3 and LaMDA. Both a generator and a discriminator are often implemented as CNNs (Convolutional Neural Networks), especially when working with images.
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 uses various machine learning techniques, such as GANs, VAEs or LLMs, to generate new content from patterns learned from training data. These outputs can be text, images, music or anything else that can be represented digitally. Generative AI is a type of artificial intelligence that can produce new, unique content based on patterns and existing data. Unlike other forms of AI that are designed to perform specific tasks, generative AI is designed to be creative and produce novel outputs that are not limited by pre-programmed rules or instructions.
Who is creating this AI, and why?
Overall, generative AI has the potential to significantly impact a wide range of industries and applications and is an important area of AI research and development. While GANs can provide high-quality samples and generate outputs quickly, the sample diversity is weak, therefore making GANs better suited for domain-specific data generation. We’ve seen that developing a generative AI model is so resource intensive that it is out of the question for all but the biggest and best-resourced companies. Companies looking to put generative AI to work have the option to either use generative AI out of the box, or fine-tune them to perform a specific task. When Priya Krishna asked DALL-E 2 to come up with an image for Thanksgiving dinner, it produced a scene where the turkey was garnished with whole limes, set next to a bowl of what appeared to be guacamole.
This tech is impressive, and it can get pretty close to writing and illustrating how a human might. Here’s a Magic School Bus short story ChatGPT wrote about Ms. Frizzle’s class trip to the Fyre Festival. And below is an illustration I asked Stable Diffusion to create about a family celebrating Hanukkah on the moon. First described in a 2017 paper from Google, transformers are powerful deep neural networks that learn context and therefore meaning by tracking relationships in sequential data like the words in this sentence. That’s why this technology is often used in NLP (Natural Language Processing) tasks. Say, we have training data that contains multiple images of cats and guinea pigs.
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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.
Other instructors might turn to lockdown browsers, which would prevent people from visiting websites during a computer-based test. The use of AI itself may become part of the assignment, which is an idea some teachers are already exploring. It’s easy to find other biases and stereotypes built into this technology, too.
- Machine learning is the ability to train computer software to make predictions based on data.
- Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data.
- The Eliza chatbot created by Joseph Weizenbaum in the 1960s was one of the earliest examples of generative AI.
- The analogy to a nature-based tree is that the data structure has various branches or might have roots that extend from a base or key topic of interest.
- Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.
Like a box of chocolates, you never know for sure what you are going to get. I repeatedly exhort during my workshops on prompt engineering that you have to clearly set aside the usual deterministic same-input begets same-output expectations that one has with nearly any ordinary conventional app. As a quick backgrounder, prompt engineering or also referred to as prompt design is a rapidly evolving realm and Yakov Livshits is vital to effectively and efficiently using generative AI or the use of large language models (LLMs). This technique is definitely worthy of being mindfully considered and given proper due for anyone aiming to enhance their prompt engineering skills. I will walk you through the keystones of the Tree of Thoughts approach and also include examples to get you started on using this clever advancement.
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In 2023, the rise of large language models like ChatGPT is indicative of the explosion in popularity of generative AI as well as its range of applications. Widespread AI applications have already changed the way that users interact with the world; for example, voice-activated AI now comes pre-installed on many phones, speakers, and other everyday technology. But applications will also bubble up from employees using the tool in their daily activities. Walmart aims to learn from the people who are “doing the work at the ground level,” said David Glick, senior vice president of enterprise business services at Walmart.
There are various ways to accomplish this, of which the most common consists of making use of multi-personas. I’ve covered multi-personas previously, see the link here and the link here. The gist of multi-personas is that you tell the AI app to pretend it is several people and then get the AI to try and use those pretend people to solve a problem for you. A commanding apprehension to be made is that this is an anthropomorphizing of the computing process. We are ascribing potentially a sense of sentience to the computer program by reusing a word that is normally reserved for sentient beings. Referring to the computer program making use of “thoughts” is disconcerting because it overly implies that the app is able to think.
This can be a big problem when we rely on generative AI results to write code or provide medical advice. Many results of generative AI are not transparent, so it is hard to determine if, for example, they infringe on copyrights or if there is problem with the original sources from which they draw results. If you don’t know how the AI came to a conclusion, you cannot reason about why it might be wrong.
Media organizations can use generative AI to improve their audience experiences by offering personalized content and ads to grow revenues. Gaming companies can use generative AI to create new games and allow players to build avatars. You could also use Bing’s chatbot to ask follow-up questions to better refine your search results. The resultsmay not always be accurate and you might even get insulted, as happened to a few people who pushed past Bing AI’s supposed guardrails found, but Microsoft was going full steam ahead anyway.
In logistics and transportation, which highly rely on location services, generative AI may be used to accurately convert satellite images to map views, enabling the exploration of yet uninvestigated locations. As for now, there are two most widely used generative AI models, and we’re going to scrutinize both. In other words, traditional AI excels at pattern recognition, while generative AI excels at pattern creation. Traditional AI can analyze data and tell you Yakov Livshits what it sees, but generative AI can use that same data to create something entirely new. “It’s quite a dangerous technology. I fear I may have done some things to accelerate it,” he said towards the end of Tesla Inc’s (TSLA.O) Investor Day event earlier this month. But the billionaire left the startup’s board in 2018 to avoid a conflict of interest between OpenAI’s work and the AI research being done by Telsa Inc (TSLA.O) – the electric-vehicle maker he leads.