Will AI transform law?

Will AI transform law?

A year ago, the startup DoNotPay claimed to have built a “robot lawyer” capable of arguing cases before the Supreme Court. There is no evidence that such a technology exists, and attempts to use AI write arguments have ended badly. But DoNotPay’s marketing gimmick was successful in getting wide attention, which goes to show that in the era of large language models, the idea of AI replacing lawyers seems quite plausible to many people.As we’ve written before, we think such expectations are extremely premature, and we shouldn’t read much into ChatGPT’s performance in simulated scenarios such as the bar exam.…
Read More
From Constant Firefighting to Innovation: How Databricks’s Money Team Halved Their Ops Burden in One Year!

From Constant Firefighting to Innovation: How Databricks’s Money Team Halved Their Ops Burden in One Year!

In the last year, the Databricks Money Engineering Team has embarked on an exhilarating journey, achieving nearly double our operational efficiency. We are excited to share this transformative experience with you, highlighting the specific strategies that fueled our success. In this post, we will discuss how introducing an Ops Czar reduced operational burden while at the same time empowered our engineering team. We will discuss pragmatism and Databricks first principles."In Unity, Strength": How Collective Effort and Strategic Efficiency Doubled Our CapabilitiesThe Money team is at the heart of commercializing Databricks's products, such as Workflows and Notebooks. We handle everything from…
Read More
Automatic retry function with Kotlin flows

Automatic retry function with Kotlin flows

Table of contents Short code example Why use this? My app on the Google play store Resources Programming Android with Kotlin: Achieving Structured Concurrency with Coroutines. Chapter 10 Short code example Here is the code that will allow you to make automatic retries on a flow: fun <T, R : Any> Flow<T>.mapWithRetry( action: suspend (T) -> R, predicate: suspend (R, attempt: Int) -> Boolean ) = map { data -> var attempt = 0L var shallRetry: Boolean var lastValue: R? = null do { val tr = action(data) shallRetry = predicate(tr, (++attempt).toInt()) if (!shallRetry) lastValue = tr } while (shallRetry)…
Read More
DataRobot introduces observability with real-time intervention capability for generative AI – SiliconANGLE

DataRobot introduces observability with real-time intervention capability for generative AI – SiliconANGLE

Artificial intelligence startup DataRobot Inc. today announced updates to its generative AI offering that will give businesses and developers observability features with real-time intervention capabilities to increase confidence when running models in production environments. The new capabilities allow enterprise teams to manage generative AI models running in all environments, including cloud, on-premises and the cloud, to execute them in production confidently while managing risk. The company said the new cross-environment AI observability will provide teams unified governance for all predictive and generative AI assets. “The No. 1 thing that I hear when I talk to customers is what we call the confidence problem,” Chief Technology Officer Michael Schmidt told…
Read More
Anthropic’s Generative AI Research Reveals More About How LLMs Affect Security and Bias

Anthropic’s Generative AI Research Reveals More About How LLMs Affect Security and Bias

Because large language models operate using neuron-like structures that may link many different concepts and modalities together, it can be difficult for AI developers to adjust their models to change the models’ behavior. If you don’t know what neurons connect what concepts, you won’t know which neurons to change. On May 21, Anthropic created a remarkably detailed map of the inner workings of the fine-tuned version of its Claude 3 Sonnet 3.0 model. With this map, the researchers can explore how neuron-like data points, called features, affect a generative AI’s output. Otherwise, people are only able to see the output…
Read More
The impact and challenges of generative AI in healthcare

The impact and challenges of generative AI in healthcare

Generative AI is transforming multiple sectors, including healthcare, where its capabilities to synthesize and analyze vast data types are being leveraged by major tech companies and innovative startups.In a significant collaboration, Google Cloud is working with Highmark Health to create artificial intelligence tools aimed at improving patient intake processes. Amazon’s AWS is exploring the uses of generative AI for social health determinants analysis in medical databases. Microsoft Azure's collaboration with the Providence healthcare network focuses on AI systems that prioritize and manage patient communications automatically.The startup ecosystem is buzzing with activity around generative AI: Ambience Healthcare is developing an app…
Read More
Cohere launches open weights AI model Aya 23 with support for nearly two dozen languages

Cohere launches open weights AI model Aya 23 with support for nearly two dozen languages

Join us in returning to NYC on June 5th to collaborate with executive leaders in exploring comprehensive methods for auditing AI models regarding bias, performance, and ethical compliance across diverse organizations. Find out how you can attend here. Today, Cohere for AI (C4AI), the non-profit research arm of Canadian enterprise AI startup Cohere, announced the open weights release of Aya 23, a new family of state-of-the-art multilingual language models. Available in 8B and 35B parameter variants (parameters refer to the strength of connections between artificial neurons in an AI model, with more generally denoting a more powerful and capable model).…
Read More
Robots’ and prosthetic hands’ sense of touch could be as fast as humans

Robots’ and prosthetic hands’ sense of touch could be as fast as humans

Research at Uppsala University and Karolinska Institutet could pave the way for a prosthetic hand and robot to be able to feel touch like a human hand. Their study has been published in the journal Science. The technology could also be used to help restore lost functionality to patients after a stroke. "Our system can determine what type of object it encounters as fast as a blindfolded person, just by feeling it and deciding whether it is a tennis ball or an apple, for example," says Zhibin Zhang, docent at the Department of Electrical Engineering at Uppsala University. He and…
Read More
No widgets found. Go to Widget page and add the widget in Offcanvas Sidebar Widget Area.