The future is already here: 5 tips for using AI

Published

16 hours ago

September 1, 2025

The Editors

The future is already here: 5 tips for using AI | The Jerusalem Post

Search, translation, health services, and smart assistants are here – but misuse of AI can risk privacy, safety, and reliability. Here are 5 tips for safe and smart use.

GPT Chat. The right prompt leads to the correct results

(photo credit: SHUTTERSTOCK)

ByLIOR NOVIK/MAARIV

SEPTEMBER 1, 2025 22:45

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Humanoid robots lack data to keep pace with explosive rise of AI

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31 minutes ago

September 2, 2025

Atharva Gosavi

Greece recently witnessed the world’s first International Humanoid Olympiad in Olympia, where humanoid robots played boxing and soccer matches to attain glory.

The event, held from August 29 to September 2, was organized by Acumino and Endeavor, who invited industry leaders to line up as speakers, apart from the smart machines displaying their abilities.

While humanoid robots have increasingly gained popularity for mirroring human actions, we have yet to see them involved in routine household chores like washing dishes and tidying closets.

Comparisons with AI

AI has advanced explosively in the past year through applications like ChatGPT, but the same cannot be said about its physical cousins – the humanoid robots. Humanoid robots are miles behind in learning from data compared to AI software and tools.

Minas Liarokapis, a Greek academic and startup founder who organized the Olympiad, made a rather bold prediction regarding humanoids becoming a helping hand in the kitchens and other household chores.

“I really believe that humanoids will first go to space and then to houses … the house is the final frontier,” she told the Associated Press (AP) on Tuesday.

“To enter the house, it’ll take more than 10 years. Definitely more,” said Liarokapis.

“I’m talking about executing tasks with dexterity, not about selling robots that are cute and are companions,” she continued.

Pinpointing the AI advantage

Any AI tool or software needs vast data for training to perform at its best. Fortunately, there’s colossal data available for training with such tools. The same, however, cannot be said for humanoids and robots.

Humanlike robots are roughly 100,000 years behind AI in learning from data, all thanks to that large divide in data availability.

Ken Goldberg, a University of California, Berkeley professor, devised a novel solution to bridge this gap. He has urged makers to go beyond simulations and make robots “collect data as they perform useful work, such as driving taxis or sorting packages.”

As it happens, researchers and scientists are already using reinforcement learning as a means to help humanoid robots learn from data in real time. This technology has helped them save valuable time by programming the machines for every action at every step.

Developing a robotic brain

The Olympiad event also hosted Hon Weng Chong, CEO of Cortical Labs, as one of the esteemed personalities in the lineup of speakers.

Chong revealed that his biotech company is developing a biological computer brain that will learn like humans.

This brain uses real brain cells grown on a chip for learning from data. These cells can learn and respond to information at a faster rate, helping robots think and adapt like humans.

The dire need for faster robotic learning

At the Humanoid Olympics, organizers focused on realistic challenges to ensure fair progress checks. Co-founder Patrick Jarvis noted that while events like discus or javelin were considered, they proved too complex.

High jump was also ruled out due to the need for specialized legs. Instead, competitions highlighted tasks that humanoid robots could practically achieve, ensuring meaningful demonstrations of capability.

However, those limitations are also a stark reminder of why faster learning is essential for humanoid robots to rival the rise of AI software and tools. Bridging that gap will decide whether humanoid robots remain niche performers or evolve into everyday companions alongside advanced AI.

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AI Research

Oxford University and Ellison Institute link for AI vaccine research

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43 minutes ago

September 2, 2025

GlobalData

The University of Oxford has announced the launch of a new vaccine research programme in collaboration with the Ellison Institute of Technology (EIT), following the receipt of £118m ($159.2m) in research funding.

The initiative, named CoI-AI (Correlates of Immunity-Artificial Intelligence), will be led by the Oxford Vaccine Group.

This programme aims to integrate Oxford’s expertise in human challenge studies, immune science and vaccine development with EIT’s advancements in AI technology.

The objective is to enhance the understanding of the immune response to infections and the protective effects of vaccines.

The CoI-AI programme will focus on the immune system’s reaction to pathogens that lead to severe infections and contribute to antibiotic resistance, including Streptococcus pneumoniae, Staphylococcus aureus and E coli.

These pathogens are responsible for widespread illnesses and have proven challenging for traditional vaccine strategies.

Researchers will employ human challenge models, where volunteers are safely exposed to specific bacteria in controlled environments, alongside modern immunology and AI methodologies to identify immune responses that correlate with protection.

Oxford Vaccine Group director Professor Sir Andrew Pollard stated: “This programme addresses one of the most urgent problems in infectious disease by helping us to understand immunity more deeply to develop innovative vaccines against deadly diseases that have so far evaded our attempts at prevention.

“By combining advanced immunology with artificial intelligence, and using human challenge models to study diseases, CoI-AI will provide the tools we need to tackle serious infections and reduce the growing threat of antibiotic resistance.”

In December 2024, Oxford and EIT formalised a long-term strategic partnership aimed at addressing some of the most pressing challenges faced by humanity.

This collaboration encompasses a range of disciplines: generative biology, clinical medicine, plant science, sustainable energy and public policy.

The initiative is supported by computational resources provided by Oracle, which include a dedicated AI team and a scholars programme designed to cultivate the next generation of scientists.

EIT chairman Larry Ellison stated: “Researchers in the CoI-AI programme will use artificial intelligence models developed at EIT to identify and better understand the immune responses that predict protection.

“This vaccine development programme combines Oxford’s leadership in immunology and human challenge models with cutting-edge AI, laying the groundwork for a new era of vaccine discovery – one that is faster, smarter and better able to respond to infectious disease outbreaks throughout the world.”

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AI Research

The Smartest Artificial Intelligence (AI) Stocks to Buy With $1,000

Published

2 hours ago

September 2, 2025

Keithen Drury

AI investing is still one of the most promising trends on the market.

Buying artificial intelligence (AI) stocks after the run they’ve had over the past few years may seem silly. However, the reality is that many of these companies are still experiencing rapid growth and anticipate even greater gains on the horizon.

By investing now, you can get in on the second wave of AI investing success before it hits. While it won’t be nearly as lucrative as the first round that occurred from 2023 to 2024, it should still provide market-beating results, making these stocks great buys now.

Image source: Getty Images.

AI Hardware: Taiwan Semiconductor and Nvidia

The demand for AI computing power appears to be insatiable. All of the AI hyperscalers are spending record amounts on building data centers in 2025, but they’re also projecting to top that number in 2026. This bodes well for companies supplying products to fill those data centers with the computing power needed for processing AI workloads.

Two of my favorites in this space are Nvidia (NVDA -3.38%) and Taiwan Semiconductor Manufacturing (TSM -3.05%). Nvidia makes graphics processing units (GPUs), which have been the primary computing muscle for AI workloads so far. Thousands of GPUs are connected in clusters due to their ability to process multiple calculations in parallel, creating a powerful computing machine designed for training and processing AI workloads.

Inside these GPUs are chips produced by Taiwan Semiconductor, the world’s leading contract chip manufacturer. TSMC also supplies chips to Nvidia’s competitors, such as Advanced Micro Devices, so it’s playing both sides of the arms race. This is a great position to be in, and it has led to impressive growth for TSMC.

Both Taiwan Semiconductor and Nvidia are capitalizing on massive data center demand, and have the growth to back it up. In Q2 FY 2026 (ending July 27), Nvidia’s revenue increased by 56% year over year. Taiwan Semiconductor’s revenue rose by 44% in its corresponding Q2, showcasing the strength of both of these businesses.

With data center demand only expected to increase, both of these companies make for smart buys now.

AI Hyperscalers: Amazon, Alphabet, and Meta Platforms

The AI hyperscalers are companies that spend a significant amount of money on AI computing capacity for internal use and to provide tools for consumers. Three major players in this space are Amazon (AMZN -1.16%), Alphabet (GOOG 0.56%) (GOOGL 0.63%), and Meta Platforms (META -1.69%).

Amazon makes this list due to the boost its cloud computing division, Amazon Web Services (AWS), is experiencing. Cloud computing is benefiting from the AI arms race because it allows clients to rent computing power from companies that have more resources than they do. AWS is the market leader in this space, and it is a huge part of Amazon’s business. Despite making up only 18% of Q2 revenue, it generated 53% of Amazon’s operating profits. AWS is a significant beneficiary of AI and is helping drive the stock higher.

Alphabet (GOOG 0.56%) (GOOGL 0.63%) also has a cloud computing wing with Google Cloud, but it’s also developing one of the highest-performing generative AI models: Gemini. Alphabet has integrated Gemini into nearly all of its products, including its most important, Google Search.

With the integration of generative AI into the traditional Google Search, Alphabet has bridged a gap that many investors feared would be the end for Google. This hasn’t been the case, and Alphabet’s impressive 12% growth in Google Search revenue in Q2 supports that. Despite its strong growth, Alphabet is by far the cheapest stock on this list, trading for less than 21 times forward earnings.

AMZN PE Ratio (Forward) data by YCharts

With Alphabet’s strength and strong position, combined with a cheap stock valuation, it’s an excellent one to buy now.

To round out this list, Meta Platforms (META -1.69%) is another smart pick. It’s the parent company of social media platforms Facebook and Instagram, and gets a huge amount of money from ads. As a result, it’s investing significant resources into improving how AI designs and targets ads, and it’s already seeing some effects. AI has already increased the amount of time users spend on Facebook and Instagram, and is also driving more ad conversions.

We’re just scratching the surface of what AI can do for Meta’s business, and with Meta spending a significant amount of money on top AI talent, it should be able to convert that into some substantial business wins.

AI is a significant boost for the world’s largest companies, and I wouldn’t be surprised to see them outperform the broader market in the coming year as a result.

Keithen Drury has positions in Alphabet, Amazon, Meta Platforms, Nvidia, and Taiwan Semiconductor Manufacturing. The Motley Fool has positions in and recommends Alphabet, Amazon, Meta Platforms, Nvidia, and Taiwan Semiconductor Manufacturing. The Motley Fool has a disclosure policy.

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