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Bridging the expectation-reality gap in machine learning

Machine learning (ML) is now mission critical in every industry. Business leaders are urging their technical teams to accelerate ML adoption across the enterprise to fuel innovation and long-term growth. But there is a disconnect between business leaders’ expectations for wide-scale ML deployment and the reality of what engineers and data scientists can actually build and deliver on time and at scale.

In a Forrester study launched today and commissioned by Capital One, the majority of business leaders expressed excitement at deploying ML across the enterprise, but data scientist team members said they didn’t yet have all the necessary tools to develop ML solutions at scale. Business leaders would love to leverage ML as a plug-and-play opportunity: “just input data into a black box and valuable learnings emerge.” The engineers who wrangle company data to build ML models know it’s far more complex than that. Data may be unstructured or poor quality, and there are compliance, regulatory, and security parameters to meet.

Applying a neuroscientific lens to the feasibility of artificial consciousness

The rise in capabilities of artificial intelligence (AI) systems has led to the view that these systems might soon be conscious. However, we might be underestimating the neurobiological mechanisms underlying human consciousness.

Modern AI systems are capable of many amazing behaviors. For instance, when one uses systems like ChatGPT, the responses are (sometimes) quite human-like and intelligent. When we, humans, are interacting with ChatGPT, we consciously perceive the text the model generates, just as you are currently consciously perceiving this text here.

The question is whether the language model also perceives our text when we prompt it. Or is it just a zombie, working based on clever pattern-matching algorithms? Based on the text it generates, it is easy to be swayed that the system might be conscious.

Machine learning gives users ‘superhuman’ ability to open and control tools in virtual reality

Researchers have developed a virtual reality application where a range of 3D modeling tools can be opened and controlled using just the movement of a user’s hand.

The researchers, from the University of Cambridge, used machine learning to develop ‘HotGestures’—analogous to the hot keys used in many desktop applications.

HotGestures give users the ability to build figures and shapes in without ever having to interact with a menu, helping them stay focused on a task without breaking their train of thought.

An AI just negotiated a contract for the first time ever — and no human was involved

In a world first, artificial intelligence demonstrated the ability to negotiate a contract autonomously with another artificial intelligence without any human involvement.

British AI firm Luminance developed an AI system based on its own proprietary large language model (LLM) to automatically analyze and make changes to contracts. LLMs are a type of AI algorithm that can achieve general-purpose language processing and generation.

Jaeger Glucina, chief of staff and managing director of Luminance, said the company’s new AI aimed to eliminate much of the paperwork that lawyers typically need to complete on a day-to-day basis.

Microsoft unveils ‘LeMa’: A revolutionary AI learning method mirroring human problem solving

The team’s research, including their code, data, and models, is now publicly available on GitHub. This open-source approach encourages the broader AI community to continue this line of exploration, potentially leading to further advancements in machine learning.

The advent of LeMa represents a major milestone in AI, suggesting that machines’ learning (ML) processes can be made more akin to human learning. This development could revolutionize sectors heavily reliant on AI, such as healthcare, finance, and autonomous vehicles, where error correction and continuous learning are critical.

As the AI field continues to evolve rapidly, the integration of human-like learning processes, such as learning from mistakes, appears to be an essential factor in developing more efficient and effective AI systems.

Former Google CEO invests in nonprofit creating an ‘AI scientist’

Eric Schmidt is funding a nonprofit that’s focused on building an artificial intelligence-powered assistant for the laboratory, with the lofty goal of overhauling the scientific research process, according to interviews with the former Google CEO and officials at the new venture.

The nonprofit, Future House, plans to develop AI tools that can analyze and summarize research papers as well as respond to scientific questions using large language models — the same technology that supports popular AI chatbots. But Future House also intends to go a step further.

The “AI scientist,” as Future House refers to it, will one day be able to sift through thousands of scientific papers and independently compose hypotheses at greater speed and scale than humans, CEO Sam Rodriques said on the latest episode of the Bloomberg Originals series AI IRL.

Humanoid robots are here, but they’re a little awkward. Do we really need them?

Building a robot that’s both human-like and useful is a decades-old engineering dream inspired by popular science fiction. While the latest artificial intelligence craze has sparked another wave of investments in the quest to build a humanoid, most of the current prototypes are clumsy and impractical, looking better in staged performances than in real life. That hasn’t stopped a handful of startups from keeping at it. The intention is not to…

Mars’ Geological History Unveiled: Curiosity Rover’s 39th Sample Reveals Clues

A recent study published in the Journal of Geophysical Research: Planets examines the 39th drilling sample collected by NASA’s Curiosity rover on Mars from a rock named “Sequoia”, which comes shortly after the pioneering robot passed its 4,000th sol, or Martian day, exploring the Red Planet. This sample was found to contain starkeyite, which is a magnesium sulfate mineral analogous to extremely dry climates such as Mars and holds the potential to help researchers better understand the climate of the Red Planet, specifically pertaining to how it got so dry.

Image of the drill hole made by NASA’s Curiosity Mars rover collect a sample on Oct. 17, 2023, the 3,980th Martian day, or sol, of the mission. (Credit: NASA/JPL-Caltech/MSSS)

“The types of sulfate and carbonate minerals that Curiosity’s instruments have identified in the last year help us understand what Mars was like so long ago. We’ve been anticipating these results for decades, and now Sequoia will tell us even more,” said Dr. Ashwin Vasavada, who is a project scientist on the Curiosity mission at NASA’s Jet Propulsion Laboratory (NASA JPL) and one of almost three dozen co-authors on the study.

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