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Finally, the goal of any healthcare organization is to provide the best possible care to patients. Predictive AI can contribute significantly to this goal by enabling more accurate diagnoses, tailored treatment plans and earlier interventions.

From the patient’s perspective, this translates to better health outcomes, reduced hospital stays and increased satisfaction with their care. For healthcare organizations, improved patient experiences lead to higher patient retention rates, positive word-of-mouth referrals and better performance on patient satisfaction metrics, which are increasingly tied to reimbursement rates in many healthcare systems.

As we’ve explored, the benefits of predictive AI extend far beyond improved diagnostics and treatment plans. It’s a catalyst for operational excellence, financial optimization, availability of investments and long-term growth. From resource management to building an authoritative brand, predictive AI touches every aspect of the healthcare business environment.

WASHINGTON (AP) — Imagine a customer-service center that speaks your language, no matter what it is.

Alorica, a company in Irvine, California, that runs customer-service centers around the world, has introduced an artificial intelligence translation tool that lets its representatives talk with customers who speak 200 different languages and 75 dialects.

So an Alorica representative who speaks, say, only Spanish can field a complaint about a balky printer or an incorrect bank statement from a Cantonese speaker in Hong Kong. Alorica wouldn’t need to hire a rep who speaks Cantonese.

Investors are pouring hundreds of billions of dollars into the AI industry right now, and much of that is going toward the development of a still theoretical technology: artificial general intelligence.

OpenAI, the maker of the buzzy chatbot ChatGPT, has made creating AGI a top priority. Its Big Tech competitors, Google, Meta, and Microsoft, are also devoting their top researchers to the same goal.

Let’s also emphasise ethical considerations. The Dartmouth participants didn’t spend much time discussing the ethical implications of AI. Today, we know better, and must do better.

We must also refocus research directions. Let’s emphasise research into AI interpretability and robustness, interdisciplinary AI research and explore new paradigms of intelligence that aren’t modelled on human cognition.

Finally, we must manage our expectations about AI. Sure, we can be excited about its potential. But we must also have realistic expectations, so that we can avoid the disappointment cycles of the past.

Robotic forearm designed with human-like proportions and efficient heat dissipation:


To replicate this in robots, researchers developed a compact forearm with a radioulnar joint using miniature bone–muscle modules. The design mimics human proportions, with two modules in the radius and ulna, totaling eight muscles. These muscles control six degrees of freedom (DOFs), including the radioulnar joint, radiocarpal joint, and finger movements.

The module’s compact design maintains the correct body proportions and weight ratios while offering more muscle-driven freedom than other robots. The researchers successfully created a forearm that closely mirrors human joint performance, allowing for precise, skillful movements similar to those of a human.

Researchers tested Kengoro, a robot equipped with a human-mimetic radioulnar forearm, by performing tasks like soldering, opening a book, turning a screw, and swinging a badminton racket.

Last week, OpenAI published the GPT-4o “scorecard,” a report that details “key areas of risk” for the company’s latest large language model, and how they hope to mitigate them.

In one terrifying instance, OpenAI found that the model’s Advanced Voice Mode — which allows users to speak with ChatGPT — unexpectedly imitated users’ voices without their permission, Ars Technica reports.

“Voice generation can also occur in non-adversarial situations, such as our use of that ability to generate voices for ChatGPT’s advanced voice mode,” OpenAI wrote in its documentation. “During testing, we also observed rare instances where the model would unintentionally generate an output emulating the user’s voice.”