ChatGPT can readily traverse the vast amounts of information on the internet to answer almost any ad-hoc question users pose. That it does so via natural language, in close to real-time, is indicative of the immense advancements of Generative Artificial Intelligence--and of Natural Language Generation, in particular. ChatGPT's practical utility spans most tasks associated with language, including creating annotated training datasets for data scientists, to creating highly specific reports, emails, or papers for almost any facet of business or academia. Not surprisingly, vendors of all types are rushing to implement this language model to improve solutions for everything from Business Intelligence to content services. "It's still got its own set of limitations," admitted Abhishek Gupta, Principal Data Scientist, and Engineer at Talentica.

It's easy to think of LLMs (large language models) as just'hallucinating' or mere generators of text. A glorified LSTM so to speak. While there are some limitations of LLMs (and indeed they are evolving), a far more interesting question to explore is: How can LLMs be used in enterprise applications? In many ways, enterprise applications of LLMs can overcome some of the problems. One possible solution is a combination of Azure Cognitive Search and Azure OpenAI Service. Taking a B2B perspective, the solution involves "chatting with your own data".

The news about ChatGPT is everywhere, and like many businesses, you're probably wondering: What is it? Does it have capabilities that can benefit my business? Is it something we need to consider? We recently announced our own integration with GPT, the large language model (LLM) that ChatGPT is built on, and you'll be able to see how it works in our upcoming NLP LiveStream on Thursday, March 2 at 11 AM EST (Save the date to join us!). By way of background, here is some information to help sort through the current ChatGPT buzz and understand if and how ChatGPT, and by extension large language models (LLMs), can be leveraged within your enterprise.

Salesforce is making a big deal this week of building OpenAI's GPT3 technology -- which powers ChatGPT -- into a broad swathe of its products, describing its Einstein GPT offering as "the world's first generative AI CRM technology." But as I explored in an interview published yesterday with Emergence Capital's Jake Saper, there are big risks in using these Large Language Models (LLMs) in a business context. I spent the day investigating whether Salesforce is cognizant of those risks, and what steps it is taking to ensure its customers don't fall foul of them when implementing solutions based on Einstein GPT. On the face of it, generative AI looks like it can bring a massive boost to business productivity, by making it easier to summarize information from unstructured data stored in documents, knowledgebases and message streams, preparing ready-made drafts for messages, emails and web content used in sales, service and marketing, or generating chunks of code and test routines for developers. But in more than twenty-five years of writing about and reporting on technology, I've seen enough to know that it's always sensible to look behind the hype and the enthusiastic demos to figure out what are the hidden downsides -- where could it all go wrong?

Check out all the on-demand sessions from the Intelligent Security Summit here. Generating digital humans (avatars) is a process increasingly making use of artificial intelligence (AI). And, the power of generative AI is now coming to avatars. This could have wide-ranging implication for enterprises, including customer support and experience. Today, Israeli startup D-ID announced the launch of its new chat.d-id

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. "Deepfake implies unauthorized use of synthetic media and generative artificial intelligence -- we are authorized from the get-go," she told VentureBeat. She described the Tel Aviv- and New York-based Hour One as an AI company that has also "built a legal and ethical framework for how to engage with real people to generate their likeness in digital form." It's an important delineation in an era when deepfakes, or synthetic media in which a person in an existing image or video is replaced with someone else's likeness, has gotten a boatload of bad press -- not surprisingly, given deepfakes' longstanding connection to revenge porn and fake news. The term "deepfake" can be traced to a Reddit user in 2017 named "deepfakes" who, along with others in the community, shared videos, many involving celebrity faces swapped onto the bodies of actresses in pornographic videos.

Artificial intelligence (AI) and machine learning (ML) are ubiquitous in consumers' lives, from the "up next" suggestions from your streaming service to routes suggested by your GPS when you plug an address into your phone for directions. Less visible impacts of AI and ML include the use of AI to control data center efficiency and cooling or the management of restaurant wait times, as some companies use AI to make decisions about how many burgers to cook for the day's lunch rush. Whereas AI refers to the ability of a computer to emulate human decision-making, ML is the algorithm-driven foundation that enables AI. We can think of automation as the application of AI to develop a series of repeatable tasks or actions designed to accomplish a certain task or execute a process. Companies use automation for transporting products to warehouse workers for packing, processing invoices, and assisting with many other repetitive business tasks that humans have historically performed.

There is plenty of talk about artificial intelligence in the enterprise, but a lot of it is not very practical. That's because enterprises aren't equipped with an army of data scientists to build and train new AI models. And it's not just the lack of qualified data scientists -- AI breakthroughs require massive amounts of relevant, annotated data. That doesn't mean however, there is no place for AI in your enterprise innovation strategy. Savvy CIOs are using in-market models and APIs by commercial and industry leaders to solve well-defined use cases, bringing immediate, measurable value to the organization.

January 17, 2020 Written by: John R. Smith IBM Research has a long history as a leader in the field of Artificial Intelligence (AI). IBM's pioneering work in AI dates back to the field's inception in the 1950s, when IBM developed one of the first instances of machine learning, which was applied to the game of checkers. Since then, IBM has been responsible for achieving major milestones in AI, ranging from Deep Blue – the first chess-playing computer to defeat a reigning world champion, to Watson – the first natural language question and answering system able to win at Jeopardy!, to last year's Project Debater – the first AI system that can build persuasive arguments on its own and effectively engage in debates on complex topics. IBM's leadership in AI continued in earnest in 2019, which was notable for a growing focus on critical topics such as making trustworthy AI work in practice, creating new AI engineering paradigms to scale AI for a broader use, and continuing to advance core AI capabilities in language, speech, vision, knowledge & reasoning, human-centered AI, and more. While recent years have seen incredible progress in "narrow AI," built on technologies like deep learning, IBM Research pushed its AI research in 2019 towards developing a new foundational underpinning of AI for enterprise applications by addressing important problems like learning more from less, enabling trusted AI by ensuring the fairness, explainability, adversarial robustness, and transparency of AI systems, and integrating learning and reasoning as a way to understand more in order to do more.

Today's push behind machine learning and artificial intelligence is so powerful that it has led to some pretty high expectations for performance and deliverables. When it comes to business value and ROI, can it really live up to the claims? Evaluating the value of a full machine learning project requires moving past the hype and managing the realities of this evolving technology, one example at the time. We'll look at the realities of a pure machine learning approach through the lens of a typical enterprise use case. If we're to believe the past couple of years' worth of marketing hype, machine learning is a magic box, supported by an evolved approach, strengthened by the latest technology and most importantly, able to effortlessly produce results.