Elon Musk's brain-chip startup Neuralink live-streamed its first patient implanted with a chip playing online chess. Noland Arbaugh, the 29-year-old patient who was paralyzed below the shoulder after a diving accident, was playing chess on his laptop and moving the cursor using the Neuralink device. He had received an implant from the company in January and could control a computer mouse using his thoughts, Musk said last month. "The surgery was super easy," Arbaugh said in the video streamed on Musk's social media platform X, referring to the implant procedure. "I literally was released from the hospital a day later. I have no cognitive impairments."

Deep Brain Stimulation therapies have proven an invaluable treatment option for patients suffering from otherwise debilitating diseases like Parkinson's. However, it -- and its sibling tech, brain computer interfaces -- currently suffer a critical shortcoming: the electrodes that convert electron pulses into bioelectric signals don't sit well with the surrounding brain tissue. And that's where folks with the lab coats and holding squids come in! In We Are Electric: Inside the 200-Year Hunt for Our Body's Bioelectric Code, and What the Future Holds, author Sally Adee delves into two centuries of research into an often misunderstood and maligned branch of scientific discovery, guiding readers from the pioneering works of Alessandro Volta to the life-saving applications that might become possible once doctors learn to communicate directly with our body's cells. Excerpted from We Are Electric: Inside the 200-Year Hunt for Our Body's Bioelectric Code, and What the Future Holds by Sally Adee.

Since its inception, the Ray ecosystem has grown to include a variety of features and tools useful for training ML models on the cloud, including Ray Tune for distributed hyperparameter tuning, the Ray Cluster Launcher for cluster provisioning, and load-based autoscaling . Because Ray is a general distributed compute platform, users of Ray are free to choose among a growing number of distributed data processing frameworks, including Spark, running on the same resources provisioned by Ray for the deep learning workflow. Now in the upcoming Ludwig 0.4 release, we're integrating Dask on Ray for distributed out-of-memory data preprocessing, Horovod on Ray for distributed training, and Ray Tune for hyperparameter optimization. Ludwig running in local mode (pre v0.4): all data needs to fit in memory on a single machine.Ludwig running on a Ray cluster (post v0.4): Ray scales out preprocessing and distributed training to process large datasets without needing to write any infrastructure code in Ludwig.By leveraging Dask, Ludwig's existing Pandas preprocessing can be scaled to handle large datasets with minimal code changes, and by leveraging Ray, we can combine the preprocessing, distributed training, and hyperparameter search all within a single job running a single training script.

Ludwig is a declarative machine learning framework that makes it easy to define machine learning pipelines using a simple and flexible data-driven configuration system. Ludwig is suitable for a wide variety of AI tasks, and is hosted by the Linux Foundation AI & Data. The configuration declares the input and output features, with their respective data types. Users can also specify additional parameters to preprocess, encode, and decode features, load from pre-trained models, compose the internal model architecture, set training parameters, or run hyperparameter optimization. Ludwig will build an end-to-end machine learning pipeline automatically, using whatever is explicitly specified in the configuration, while falling back to smart defaults for any parameters that are not.

The mayors of several European capitals have been duped into holding video calls with a deepfake of their counterpart in Kyiv, Vitali Klitschko. The mayor of Berlin, Franziska Giffey, took part in a scheduled call on the Webex video conferencing platform on Friday with a person she said looked and sounded like Klitschko. "There were no signs that the video conference call wasn't being held with a real person," her office said in a statement. It was only after about 15 minutes, when the supposed Kyiv mayor at the other end of the line started to talk about the problem of Ukrainian refugees cheating the German state of benefits, and appeared to call for refugees to be brought back to Ukraine for military service, that Giffey grew suspicious. When the connection was briefly interrupted, the Berlin mayor's office contacted the Ukrainian ambassador to Germany, who confirmed through authorities in Kyiv that the person on the video call was not the real Klitschko, the news magazine Der Spiegel reported.

Data science teams are stymied by disorganization at their companies, impacting efforts to deploy timely AI and analytics projects. In a recent survey of "data executives" at U.S.-based companies, 44% said that they've not hired enough, were too siloed off to be effective and haven't been given clear roles. Respondents said that they were most concerned about the impact of a revenue loss or hit to brand reputation stemming from failing AI systems and a trend toward splashy investments with short-term payoffs. These are ultimately organizational challenges. But Piero Molino, the co-founder of AI development platform Predibase, says that inadequate tooling often exacerbates them.

We develop a clausal resolution-based approach for computing uniform interpolants of TBoxes formulated in the description logic ALC when such uniform interpolants exist. We also present an experimental evaluation of our approach and of its application to the logical difference problem for real-life ALC ontologies. Our results indicate that in many practical cases uniform interpolants exist and that they can be computed with the presented algorithm.

The overriding goal is to make ML methods easy to use and/or construct, which is especially important in the context of complex applications. In this article, we will have a look into what declarative learning is and how a Toolbox called Ludwig built by UberAI can be used in the context of it. The major points to be discussed in this article are listed below. Let's start the discussion by understanding what declarative learning is in machine learning. Declarative ML intends to simplify the usage and/or creation of ML algorithms by isolating application or algorithm semantics from the underlying data representations and execution plans, resulting in a high-level definition of ML tasks or algorithms.

Developers should not have to set hyperparameters manually or implement their custom model code unless truly necessary, as it accounts for just a tiny fraction of the project life cycle, and differences are usually tiny.

Patient receives the world's first fully 3D-printed prosthetic eye, Russia may press criminal charges in 2018 ISS pressure leak incident, UK competition regulator orders Meta to sell Giphy.