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Enzymes play a key role in cellular metabolic processes. To enable the quantitative assessment of these processes, researchers need to know the so-called "turnover number" (for short: kcat) of the enzymes. In the scientific journal Nature Communications, a team of bioinformaticians from Heinrich Heine University Dsseldorf (HHU) now describes a tool for predicting this parameter for various enzymes using AI methods.

Enzymes are important biocatalysts in all living cells. They are normally large proteins, which bind smaller molecules -- so-called substrates -- and then convert them into other molecules, the "products." Without enzymes, the reaction that converts the substrates into the products could not take place, or could only do so at a very low rate. Most organisms possess thousands of different enzymes. Enzymes have many applications in a wide range of biotechnological processes and in everyday life -- from the proving of bread dough to detergents.

The maximum speed at which a specific enzyme can convert its substrates into products is determined by the so-called turnover number kcat. It is an important parameter for quantitative research on enzyme activities and plays a key role in understanding cellular metabolism.

However, it is time-consuming and expensive to determine kcat turnover numbers in experiments, which is why they are not known for the vast majority of reactions. The Computational Cell Biology research group at HHU headed by Professor Dr Martin Lercher has now developed a new tool called TurNuP to predict the kcat turnover numbers of enzymes using AI methods.

To train a kcat prediction model, information about the enzymes and catalysed reactions was converted into numerical vectors using deep learning models. These numerical vectors served as the input for a machine learning model -- a so-called gradient boosting model -- which predicts the kcat turnover numbers.

Lead author Alexander Kroll: "TurNuP outperforms previous models and can even be used successfully for enzymes that have only a low similarity to those in the training dataset." Previous models have not been able to make any meaningful predictions unless at least 40% of the enzyme sequence is identical to at least one enzyme in the training set. By contrast, TurNuP can already make meaningful predictions for enzymes with a maximum sequence identity of 0 -- 40%.

Professor Lercher adds: "In our study, we show that the predictions made by TurNuP can be used to predict the concentrations of enzymes in living cells much more accurately than has been the case to date."

In order to make the prediction model easily accessible to as many users as possible, the HHU team has developed a user-friendly web server, which other researchers can use to predict the kcat turnover numbers of enzymes.

Link to the web server: https://turnup.cs.hhu.de/

Background: Machine learning and deep learning

Deep learning models comprise multi-layered artificial neural networks which can recognise and process patterns in the input data. Using large training datasets is the optimum way to train a deep learning model to process numerical inputs.

Gradient boosting models are a machine learning method, which produces large numbers of decision trees. The results of all decision trees for a specific input are used to make predictions. Similar to deep learning, training data are used to refine the model, i.e. to produce the decision trees.

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AI predicts the work rate of enzymes - Science Daily

Dive Brief:

As AI becomes more integrated into the workplace, legislators are scrambling to catch up. All told, more than 160 bills and regulations related to AI were in the works as of mid-June, a management-side lawyer said in June, warning of a huge tsunami coming of state regulation.

The U.S. Equal Employment Opportunity Commission in May issued guidance on how to audit AI for discrimination to remain in compliance with Title VII of the Civil Rights Act of 1964. And the EEOC, Consumer Financial Protection Bureau, U.S. Department of Justice and Federal Trade Commission in April outlined to businesses how existing laws can apply to emerging technologies like AI.

Meanwhile, New York City started enforcing a new law requiring employers to perform bias audits on automated employment decision tools and to notify candidates of the use of those tools in their hiring process.

Amid the changing AI landscape, employers are trying to figure out how the new technology affects their hiring needs. Human cognitive skills like problem solving, creativity, originality, imagination and the ability to learn will remain in demand, even as automated tools transform the workplace, according to a July 13 report from TalentLMS. Similarly, in the Cengage Group report, 59% of employers said AI has led them to prioritize different skills in the hiring process, including uniquely human skills.

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AI puts glitch in graduates' employment plans - Higher Ed Dive

Chinese tech giants are speeding to catch up with their US peers in the creation of artificial intelligence (AI) chatbots. The worlds second-largest economy is on course to spend $15 billion (13.5 billion) on AI projects this year alone, a rise of nearly 50% in just two years.

Even before the arrival of large language models like the Microsoft-backed Chat GPT, some tech experts refused to bet that the West would dominate the AI race, despite the most advanced AI labs being located in the US and UK.

Kai-Fu Lee, the Taiwanese computer scientist, venture capitalist and tech executive predicted in 2018 that China would quickly surpass the US as an AI superpower, insisting the technology had already passed the innovation stage.

Lee argued that the world was now in the AI implementation stage, where China has the edge, due to years of state surveillance. Snooping on the Chinese population has allowed the accrual of huge amounts of data, which AI platforms harness to improve their learning.

Strong growth ahead

But while more than half of the estimated 1 billion surveillance cameras on the planet have been deployed in China, Lees critics argue that the AI revolution is still in its infancy and the West still holds the key.

The big innovations in AI havent happened yet far from it [] and the US currently has the advantage in that area, Pedro Domingos, professor emeritus at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, told DW.

Domingos spoke of large data having diminishing returns, in that China wont get a lot more benefit from snooping on its 1.4 billion population than the US does, with its 332 million inhabitants.

The diversity of the data also matters. I would prefer to have the data from, say, Europe than China as it is more diverse and therefore you can learn more from it.

The US is clearly troubled by Beijings technological ambitions, especially as the official Chinese government policy is to make the country the worlds dominant AI player by 2030.

Chip curbs delays Beijings ambitions

Ever-worsening relations between Washington and Beijing led the US last year to put export curbs on the most advanced memory chips, which Chinese firms need for their own advanced AI language models.

The most cutting-edge AI systems require massive amounts of hardware thousands of very specialized chips, running for weeks or months at a time, Paul Scharre, executive vice president and director of studies at the Center for a New American Security, told DW.

Denying China access will shut them out of building the most advanced systems and that gap is likely to widen over time as chip technology continues to advance.

Chinese tech firms may find other ways around the ban. The domestic semiconductor market is likely to see an investment boom as local producers race to improve their own chips.

Is the West giving it away?

Another hole Washington may seek to close is how the US machine learning platforms are currently open source freely available to be copied and modified.

If you have access to the trained AI model you dont need the advanced chips. So there is a real risk that the export controls will become ineffective, Scharre, the author of the book Four Battlegrounds: Power in the Age of Artificial Intelligence, warned.

Indeed, Chinas own versions of Chat GPT, created by the likes of e-commerce giant Alibaba and social media platform Baidu, were released to the world in April, just months after their US rivals.

Chinese tech sector tamed

But the country has lots of other hurdles to overcome before it rules the AI space. For one, President Xi Jinpings crackdown on the power of the tech sector over the past two years likely made Chinese executives more risk-averse.

Youve seen an explosion in rules and enforcement for the tech sector, Karman Lucero, a fellow at the Paul Tsai China Center, at Yale Law School, told DW. Often it [the enforcement] has been very opaque and has even preceded the new rules, which has a chilling effect on the industry.

Lucero noted how the Chinese governments obsession with censorship could be their Achilles heel as AI models wont realize their full learning potential with large amounts of missing data or when programmed to avoid many off-limit topics.

In China, you have this wide range of [censorable] content that is always shifting. Something that could be a permissible topic today could be prohibited tomorrow, and theres no way to predict it.

Brain drain inevitable

The country is also short of skilled workers needed to achieve Beijings goals. Despite an effort to build an army of AI talent, retaining top tech workers is a challenge when their skills are in demand globally.

Talent exodus is a major hindrance to Chinas authoritarianism in that it drives people away. Chinas top AI scientists leave and its not just that they go abroad to study and work, they prefer a more democratic way of life, Scharre said.

Despite these issues, he thinks Chinas AI labs are just 18 months behind the current leading research labs in the West and that the country already has the edge when it comes to deploying AI across society.

China undeterred

While some in the West still question whether the rollout of AI should be paused over safety concerns, Domingos says China is going full steam ahead in exploiting what he said was a dream tool for an autocrat.

For us in the democratic world, its absolutely essential that the US comes out ahead. If China does, were in a lot of trouble politically, economically and militarily, he warned.

In the same way that US culture and technology spread globally over the past century, Domingos argued that Chinas domination of AI would make the rest of the world more like them, giving the example of how the West quickly followed China into strict lockdowns when COVID first hit.

In some ways, it may be harmless there are many great things about China but as far as their ideology goes, it is very dangerous.

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AI: How far is China behind the West? - The Indian Express

Artificial intelligence (AI) is for real. The amazing progress in AI -- and the skyrocketing share prices of AI leaders -- has proven it over the last several months.

My portfolio is worth considerably more now than at the end of 2022 because of the AI boom. I like several AI stocks. But if I could buy only one AI stock right now, which would it be?

I only have to glance at the stocks that have been big winners for me to find several top AI contenders. Microsoft made a brilliant move by investing heavily in OpenAI and partnering with the company to integrate ChatGPT into its own products. My only regret with Microsoft is that I wish I had owned more shares.

Nvidiahas absolutely crushed it this year. We might have to change the old saying that something is "selling like hotcakes" to "selling like GPUs," the graphics processing units Nvidia markets that are ideal for powering AI systems.

Meta Platformsis another great performer so far in 2023. The company is best known for its social media platform, which now includes the wildly popular Threads app. But Meta's open-source approach has made it a serious contender in AI as well.

Amazon has made its mark in AI, too. It has introduced multiple new AI products that should make its Amazon Web Services cloud platform even more attractive to developers.

There are also quite a few promising AI stocks that I haven't bought. However, the one AI stock I'd buy right now is already a major holding for me -- Google parent Alphabet (GOOG 1.30%) (GOOGL 1.26%).

Alphabet clears several hurdles in a way that the other contenders don't.Valuation stands at the top of the list.

My colleague Travis Hoium thinks that Alphabet stock is "insanely cheap." I wouldn't go quite that far, but I certainly agree with the overall sentiment, especially in comparison with most of the other AI leaders.

Alphabet shares trade at a forward price-to-earnings ratio of less than 23. That's a bargain price when you stack it up against Nvidia's forward earnings multiple of 62. If a big pullback is on the way (and I suspect one is), Alphabet stock should fare better than many of its high-flying brethren.

I also think Alphabet has firmly established its AI credentials. Some thought it could be in trouble after the early success of ChatGPT and the less-than-stellar debut of the company's own Google Bard. However, Alphabet has held its own. I have personally seen the company dramatically improve Bard over time as well as introduce other impressive AI innovations.

Alphabet's financial position is another huge plus, in my view. It's highly profitable. The company has a cash stockpile of more than $115 billion.

Finally, Alphabet is arguably the top leader in one area that could turbocharge AI advances and adoption -- quantum computing. There's still a long way to go before quantum computing technology might achieve its potential. Maybe it never will. But if any company can make it happen, it's Alphabet.

Some argue that Alphabet is a risky proposition because of AI. They maintain that Google Search could be disrupted by chatbots or other AI apps. I understand those concerns, but I believe that Alphabet is less risky than they think.

For one thing, Google Search's market share has held relatively steady, according to Bank of America analysts Justin Post and Joanna Zhao. A BofA survey even found that 45% of ChatGPT users will use Google Search even more with the integration of generative AI.

Anecdotally, I've found that I use Google Search as much as I ever have with subjects that tend to attract advertisers. And I use ChatGPT, Bard, and other chatbots quite a bit. I also have access to Google Search's experimental conversational AI integration and find it helpful.

My view is that the biggest risk for any AI stock over the near term is related to valuation. As previously mentioned, Alphabet competes especially well on this front.

I predict that multiple stocks will be huge winners over the next decade and beyond with the increased adoption of AI. But at least for right now, in my opinion, Alphabet is the most attractive of the bunch.

Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fool's board of directors. Randi Zuckerberg, a former director of market development and spokeswoman for Facebook and sister to Meta Platforms CEO Mark Zuckerberg, is a member of The Motley Fool's board of directors. Bank of America is an advertising partner of The Ascent, a Motley Fool company. Keith Speights has positions in Alphabet, Amazon.com, Bank of America, Meta Platforms, and Microsoft. The Motley Fool has positions in and recommends Alphabet, Amazon.com, Bank of America, Meta Platforms, Microsoft, and Nvidia. The Motley Fool has a disclosure policy.

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If I Could Buy Only 1 AI Stock Right Now, This Would Be It - The Motley Fool

[1/3]A Microsoft logo is seen in Los Angeles, California U.S. November 7, 2017. REUTERS/Lucy Nicholson/File Photo

July 24 (Reuters) - U.S. tech giants could signal an end to the nearly year-long slowdown in their cloud businesses as signs of economic resilience encourage clients to boost technology spending, while a pickup in digital ads will also aid profits.

Microsoft, Google-owner Alphabet, Amazon.com and Meta Platforms companies that are together valued at over $6 trillion are set to report earnings this week and the next, in what will be a test for their hefty valuations and the broader market rally they have driven thanks to optimism over artificial intelligence.

"We're really only looking for metrics that point to ramping user traction for AI-based offerings, with the idea being that they will generate more meaningful revenue in the medium-term," Canaccord Genuity analyst Kingsley Crane said.

The four companies have this year aggressively integrated AI into their products on hopes that it would drive the industry's next growth cycle, but those efforts will take time to pay off.

For Amazon (AMZN.O), Microsoft (MSFT.O) and Alphabet (GOOGL.O) the three biggest players in the cloud market the April-June quarter is expected to mark another period of dismal growth in the business that has long been a cash cow.

Both Amazon and Alphabet will likely report their lowest-ever growth for the cloud computing business at 9.8% and 24.4%, respectively, according to analysts polled by Refinitiv. Meanwhile Microsoft Intelligent Cloud, home to Azure, is expected to grow at 13.7%, the slowest rate since 2017.

However, several analysts believe the trend is about to change.

"While the macro continues to be soft, it is not getting materially worse and companies are figuring out how to operate in this," RBC Capital Markets analyst Rishi Jaluria said.

The current quarter will also have easier year-ago comparisons as the cloud slowdown started in the September quarter of 2022, Jaluria added.

A recent survey by RBC Capital of more than 150 enterprise technology buyers showed that over four-fifth of them were funding projects related to generative AI and they broadly expect IT spending to increase this year over 2022.

For Facebook-owner Meta Platforms (META.O), revenue is expected to grow at its fastest pace in six quarters thanks to a pickup in the digital advertising market as consumer spending stays strong.

"If the digital ad space is like riding a roller coaster, we are just about done with the boring/tough part, slowly climbing to the top chain link by chain link," Bernstein analysts said.

The digital ad market recovery will also aid Alphabet, whose Google Search has so far avoided any meaningful market share loss to Microsoft's AI-powered Bing.

Alphabet is expected to report 4.5% revenue growth in the April-June period, its best in three quarters.

"Google Search has seemingly shifted from market share risk to monetization risk, but with search share seemingly healthy, Google may have less urgency to integrate LLM (large-language model) results into commercial queries," analysts at BofA Global Research said.

Microsoft and Alphabet will report quarterly results on July 25, Meta on July 26 and Amazon on Aug. 3.

Reporting by Yuvraj Malik and Aditya Soni in Bengaluru; Editing by Shounak Dasgupta

Our Standards: The Thomson Reuters Trust Principles.

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Big Tech looks to turn the corner on cloud as AI focus stays strong - Reuters