This week, artificial intelligence caught up with the future – or at least Hollywood's idea of it from a decade ago. "It feels like AI from the movies," wrote the OpenAI chief executive, Sam Altman, of his latest system, an impressive virtual assistant. To underline his point he posted a single word on X – "her" – referring to the 2013 film starring Joaquin Phoenix as a man who falls in love with a futuristic version of Siri or Alexa, voiced by Scarlett Johansson. For some experts, that new AI, GPT-4o, will be an unsettling reminder of their concerns about the technology's rapid advances, with a key OpenAI safety researcher leaving this week following a disagreement over the company's direction. For others the GPT-4o release will be confirmation that innovation continues in a field promising benefits for all. Next week's global AI summit in Seoul, attended by ministers, experts and tech executives, will hear both perspectives, as underlined by a safety report released before the meeting that referred to potential positives as well as numerous risks.

In just a few months, you'll be able to ask a virtual assistant to transcribe meeting notes during a work call, summarize long email threads to quickly draft suggested replies, quickly create a specific chart in Excel, and turn a Word document into a PowerPoint presentation in seconds. Over the past week, a rapidly evolving artificial intelligence landscape seemed to leap ahead again. Microsoft and Google each unveiled new AI-powered features for their signature productivity tools and OpenAI introduced its next-generation version of the technology that underpins its viral chatbot tool, ChatGPT. Suddenly, AI tools, which have long operated in the background of many services, are now more powerful and more visible across a wide and growing range of workplace tools. Google's new features, for example, promise to help "brainstorm" and "proofread" written work in Docs.

Even if you haven't tried artificial intelligence tools that can writing essays and poems or conjure new images on command, chances are the companies that make your household products are already starting to do so. Mattel has put the AI image generator DALL-E to work by having it come up with ideas for new Hot Wheels toy cars. Used vehicle seller CarMax is summarizing thousands of customer reviews with the same "generative" AI technology that powers the popular chatbot, ChatGPT. Meanwhile, Snapchat is bringing a chatbot to its messaging service. And the grocery delivery company Instacart is integrating ChatGPT to answer customers' food questions.

Even if you haven't tried artificial intelligence tools that can write essays and poems or conjure new images on command, chances are the companies that make your household products are already starting to do so. Mattel has put the AI image generator DALL-E to work by having it come up with ideas for new Hot Wheels toy cars. Used vehicle seller CarMax is summarizing thousands of customer reviews with the same "generative" AI technology that powers the popular chatbot ChatGPT. Meanwhile, Snapchat is bringing a chatbot to its messaging service. And the grocery delivery company Instacart is integrating ChatGPT to answer customers' food questions. Coca-Cola plans to use generative AI to help create new marketing content.

Check out all the on-demand sessions from the Intelligent Security Summit here. Exactly three weeks ago, OpenAI released ChatGPT. Since then, it has been nearly impossible to keep up with both the hyped-up excitement and brow-furrowing concerns around use cases for the text-generating chatbot, ranging from the fun (writing limericks and rap lyrics) and the clever (writing prompts for text-to-image generators like DALL-E and Stable Diffusion) to the dangerous (threat actors using it for generating phishing emails) and the game-changing (could Google's entire search model [subscription required] be upended?). Is it possible to compare this moment in the evolution of generative AI to any other technology development? According to Forrester Research AI/ML analyst Rowan Curran, it is.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. In the wake of last week's release of the DALL-E API, a crowd of startups is sure to follow, racing to build generative AI for the enterprise. I thought of this as I watched coverage of 50,000 runners converging on New York City for its annual marathon yesterday. It reminded me of OpenAI's announcement last week about its Converge program, which will provide 10 early-stage startups with $1 million each and early access to its systems. "I can't think of a more interesting time to start a startup in recent memory," said OpenAI Sam Altman in a tweet about the program. That announcement came just a day before the company released the hotly anticipated DALL-E API in public beta, which means developers can now integrate DALL-E directly into their apps and products -- including many that will likely be used for a host of enterprise use cases.

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. Forrester Research's recently-released predictions report for artificial intelligence highlights what most have already observed: AI adoption has evolved from an emerging, nice-to-have trend to experiment with to a legitimate, must-do priority for enterprises. Basically, get on board the AI train or be left behind. The "get on board with AI now" message has been hammered home for several years, but this year's stats do seem to point to a significant evolution: According to Forrester's Data and Analytics Survey, 2022 [subscription required], 73% of data and analytics decision-makers are building AI technologies and 74% see a positive impact on their organizations from the use of AI. No vertical industry is failing to find opportunities to implement AI, and companies at all maturity levels are transforming fundamental functions in the organization, the predictions report found, while in 2023 AI adoption will "continue to expand and be more creative, trustworthy and optimized."

AI can play many roles in the technology stack of a modern enterprise. Its performance as a neutral, data-based, analytical advisor could allow businesses to use algorithms to predict whether a decision is the right one. AI-based decisions are part of an arsenal of tools leveraged by technology high performers. Businesses led by digitally savvy leaders, those who champion emerging technologies such as AI, outperform other like-sized businesses by 48% on valuation and revenue growth, according to one MIT research study. "The integration of traditional decisioning into AI is really just starting to hit its stride right now," said Rowan Curran, analyst at Forrester.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Artificial intelligence (AI) has been steadily influencing business processes, automating repetitive and mundane tasks even for complex industries like construction and medicine. While AI applications often work beneath the surface, AI-based content generators are front and center as businesses try to keep up with the increased demand for original content. However, creating content takes time, and producing high-quality material regularly can be difficult.

Although we would like our robots to have completely autonomous behavior, this is often not possible. Some parts of a task might be hard to automate, perhaps due to hard-to-interpret sensor information, or a complex environment. In this case, using shared autonomy or teleoperation is preferable to an error-prone autonomous approach. However, the question of which parts of a task to allocate to the human, and which to the robot can often be tricky. In this work, we introduce A3P, a risk-aware task-level reinforcement learning algorithm. A3P represents a task-level state machine as a POMDP. In this paper, we introduce A3P, a risk-aware algorithm that discovers when to hand off subtasks to a human assistant. A3P models the task as a Partially Observably Markov Decision Process (POMDP) and explicitly represents failures as additional state-action pairs. Based on the model, the algorithm allows the user to allocate subtasks the robot or the human in such a way as to manage the worst-case performance time for the overall task.