Slack has become the latest developer to commit to integrating AI inside of its platform, with a new Slack GPT integration that will use chatbots as your own personal business assistants. But Slack GPT also highlights another AI quirk that may fly under the radar: its short-term attention span. In March, Salesforce's Slack division announced a ChatGPT app, allowing the OpenAI chatbot to be pulled into the Slack conversational interface. Now, Slack is expanding that: Slack GPT will support ChatGPT, the Claude chatbot, and Slack's own eventual Einstein GPT chatbot as well. To avoid forcing users to scroll back through reams of chat text (say, after returning from vacation) Slack GPT will simply summarize it all for you.

Google's acceleration comes as a cacophony of voices -- including notable company alumnae and industry veterans -- are calling for the AI developers to slow down, warning that the tech is developing faster than even its inventors anticipated. Geoffrey Hinton, one of the pioneers of AI tech who joined Google in 2013 and recently left the company, has since gone on a media blitz warning about the dangers of supersmart AI escaping human control. Pichai, along with the CEOs of OpenAI and Microsoft, will meet with White House officials on Thursday, part of the administration's ongoing effort to signal progress amid public concern, as regulators around the world discuss new rules around the technology.

The Biden administration's investment in responsible AI research and development is a $140 million grant, which will increase the number of national AI research institutes. These institutes are focused on advancing artificial intelligence research in areas ranging from public health to cybersecurity. The investment is just a fraction of the billions that private sector companies are pouring into advancing the technology. Microsoft previously invested $10 billion in OpenAI.

At its World Tour NYC event, Salesforce has introduced Slack GPT, which it describes as a three-pronged vision that integrates AI features into the business messaging app. Slack GPT is comprised of AI-powered features built natively into the app, a new AI-ready platform that was recently made available to developers, and the availability of Einstein GPT in the app that will power its ability to instantly generate insights and summaries. Einstein GPT was developed by Salesforce as a generative AI for customer relationship management (CRM) and could assist businesses with tasks related to sales. The integrated AI features will give users access to a workflow builder that doesn't require them to know how to code. In it, they can automatically create or update a canvas, which is Slack's tool designed for collaboration.

Ahead of a meeting between Vice President Kamala Harris and the heads of America's four leading AI tech companies -- Alphabet, OpenAI, Anthropic and Microsoft -- the Biden Administration announced Thursday a sweeping series of planned actions to help mitigate some of the risks that these emerging technologies pose to the American public. That includes $140 million to launch seven new AI R&D centers as part of the National Science Foundation, extracting commitments from leading AI companies to participate in a "public evaluation" of their AI systems at DEFCON 31, and ordering the Office of Management and Budget (OMB) to draft policy guidance for federal employees. "The Biden Harris administration has been leading on these issues since long before these newest generative AI products debuted last fall," a senior administration official said during a reporters call Wednesday. The Administration unveiled its AI Bill of Rights "blueprint" last October, which sought to "help guide the design, development, and deployment of artificial intelligence (AI) and other automated systems so that they protect the rights of the American public," per a White House press release. "At a time of rapid innovation, it is essential that we make clear the values we must advance, and the common sense we must protect," the administration official continued.

ChatGPT has stoked new hopes about the potential of artificial intelligence--but also new fears. Today the White House joined the chorus of concern, announcing it will support a mass hacking exercise at the Defcon security conference this summer to probe generative AI systems from companies including Google. The White House Office of Science and Technology Policy also said that $140 million will be diverted towards launching seven new National AI Research Institutes focused on developing ethical, transformative AI for the public good, bringing the total number to 25 nationwide. The announcement came hours before a meeting on the opportunities and risks presented by AI between vice president Kamala Harris and executives from Google and Microsoft as well as the startups Anthropic and OpenAI, which created ChatGPT. The White House AI intervention comes as appetite for regulating the technology is growing around the world, fueled by the hype and investment sparked by ChatGPT.

Bing AI is now open to all--sort of. Three months after debuting its revamped search engine, Microsoft has announced that it's now moving into open preview. You'll still need to sign into Bing on the Edge browser (or the Bing mobile apps) to use the chatbot, but at least you no longer have to deal with a waitlist. As if to celebrate this new phase of Bing (powered by OpenAI's GPT-4), Microsoft is also rolling out several new features. For one, it can go beyond mere text responses to deliver charts, graphs and rich formatting.

Deep generative models such as GANs, normalizing flows, and diffusion models are powerful regularizers for inverse problems. They exhibit great potential for helping reduce ill-posedness and attain high-quality results. However, the latent tensors of such deep generative models can fall out of the desired high-dimensional standard Gaussian distribution during inversion, particularly in the presence of data noise and inaccurate forward models, leading to low-fidelity solutions. To address this issue, we propose to reparameterize and Gaussianize the latent tensors using novel differentiable data-dependent layers wherein custom operators are defined by solving optimization problems. These proposed layers constrain inverse problems to obtain high-fidelity in-distribution solutions. We validate our technique on three inversion tasks: compressive-sensing MRI, image deblurring, and eikonal tomography (a nonlinear PDE-constrained inverse problem) using two representative deep generative models: StyleGAN2 and Glow. Our approach achieves state-of-the-art performance in terms of accuracy and consistency.

Traditional drug discovery programs are being transformed by the advent of machine learning methods. Among these, Generative AI methods (GM) have gained attention due to their ability to design new molecules and enhance specific properties of existing ones. However, current GM methods have limitations, such as low affinity towards the target, unknown ADME/PK properties, or the lack of synthetic tractability. To improve the applicability domain of GM methods, we have developed a workflow based on a variational autoencoder coupled with active learning steps. The designed GM workflow iteratively learns from molecular metrics, including drug likeliness, synthesizability, similarity, and docking scores. In addition, we also included a hierarchical set of criteria based on advanced molecular modeling simulations during a final selection step. We tested our GM workflow on two model systems, CDK2 and KRAS. In both cases, our model generated chemically viable molecules with a high predicted affinity toward the targets. Particularly, the proportion of high-affinity molecules inferred by our GM workflow was significantly greater than that in the training data. Notably, we also uncovered novel scaffolds significantly dissimilar to those known for each target. These results highlight the potential of our GM workflow to explore novel chemical space for specific targets, thereby opening up new possibilities for drug discovery endeavors.

Deep generative models have various content creation applications such as graphic design, e-commerce, and virtual Try-on. However, current works mainly focus on synthesizing realistic visual outputs, often ignoring other sensory modalities, such as touch, which limits physical interaction with users. In this work, we leverage deep generative models to create a multi-sensory experience where users can touch and see the synthesized object when sliding their fingers on a haptic surface. The main challenges lie in the significant scale discrepancy between vision and touch sensing and the lack of explicit mapping from touch sensing data to a haptic rendering device. To bridge this gap, we collect high-resolution tactile data with a GelSight sensor and create a new visuotactile clothing dataset. We then develop a conditional generative model that synthesizes both visual and tactile outputs from a single sketch. We evaluate our method regarding image quality and tactile rendering accuracy. Finally, we introduce a pipeline to render high-quality visual and tactile outputs on an electroadhesion-based haptic device for an immersive experience, allowing for challenging materials and editable sketch inputs.