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Generative Artificial Intelligence Revolution Heats Up in Asia/Pacific, with IDC expecting a 95.4% CAGR in 2027 … – EMSNow

April 17, 2024 | Author: admin

SINGAPORE IDCs latestWorldwide AI and Generative AI Spending Guidereveals that the Asia/Pacific* region is witnessing an unprecedented surge in Generative AI (GenAI) adoption, including software, services, and hardware for AI-centric** systems with spending projected to soar to $26 billion by 2027, with a compound annual growth rate (CAGR) of 95.4 percent for the period 2022-2027. This surge underscores the regions pivotal role in driving the next wave of AI innovation and technological advancement.

GenAI is a branch of computer science involving unsupervised and semi-supervised algorithms that enable computers to create new content using previously created content, such as text, audio, video, images, and code, in response to short prompts. IDC believes GenAI will be a trigger technology to transition to a new chapter in the move toward automation for both internal and external parties across generic productivity, business functionspecific enhancements, or industry-specific tasks.

We anticipate that Asia/Pacific will experience a surge in the adoption of Generative AI, with growth rates expected to match those of North America, largely due to enterprises investing heavily in developing data and infrastructure platforms tailored for GenAI applications. We forecast that this investment in GenAI will reach its zenith within the next two years, followed by a period of stabilization. China is projected to maintain its position as the dominant market for GenAI, while Japan and India are set to become the most rapidly expanding markets in the forthcoming years,Deepika Giri, Head of Research, Big Data & AI, IDC APJ.

Unlocking the vast potential of GenAI, the Asia/Pacific region is poised for a transformative journey across various sectors. With robust digital infrastructure and growing investments in technology, Asia/Pacific emerges as a pivotal player in this dynamic landscape. Strategic investment in hardware, software, and associated services for GenAI is crucial to sustaining and propelling this progress. From software development to customer service, GenAI is revolutionizing industries, ushering in a new era of innovation in Asia/Pacific.

IT spending in GenAI technology progresses through three distinct stages. Initially, during the GenAI Foundation Build phase, attention is directed towards enhancing core infrastructure, investing in IaaS, and bolstering security software. Subsequently, in the Broad Adoption phase, the focus shifts towards the widespread adoption of open-source AI platforms offered as-a-service, playing a fundamental role in digital business control planes. Finally, the Unified AI Services phase sees a surge in spending as organizations rapidly integrate GenAI to gain a competitive edge, diverging from the typical slower growth observed in new technology markets.

GenAI isnt a fleeting trend. Its capacity to generate entirely new content, across various mediums, such as images, videos, code, and marketing materials, promises substantial efficiency gains and paves the way for innovative creative opportunities, granting a competitive advantage, saysVinayaka Venkatesh, Senior Market Analyst, IT Spending Guides, Customer Insights & Analysis, IDC Asia/Pacific. A significant portion of organizations have either already adopted Generative AI or are in the initial stages of experimenting with models,Vinayaka Venkateshends.

The financial services sector is experiencing rapid growth in Generative AI adoption in Asia. It is projected to reach $4.3 billion by 2027 with a remarkable CAGR of 96.7%. Within this industry, GenAI is being utilized internally to enhance operations efficiency, automate repetitive tasks, and optimize back-office processes such as fraud detection and the creation of intricate documents. Generative AI-powered solutions provide tailored financial services like personalized planning tools and reports, which dynamically adjust to meet customers evolving needs. Furthermore, the integration of GenAI yields substantial benefits to profitability by cutting costs, driving revenue generation, and enhancing productivity across various functions such as DevOps, marketing, and legal compliance.

The software and information services industry stands as the second-largest adopter of GenAI, embracing its versatility across sectors such as marketing, data analytics, and software development. Within marketing, GenAI can streamline content creation for websites, blogs, and social media platforms, optimizing marketing strategies and enhancing audience engagement. In data-driven fields like machine learning and analytics, GenAI proves invaluable for generating synthetic data, enriching existing datasets, and improving model performance and resilience. Additionally, in software development, these tools aid developers by automating coding tasks, generating prototypes, and accelerating the software development lifecycle, leading to heightened productivity and efficiency.

As the third-largest adopter of GenAI, governments across the Asia-Pacific region have a substantial opportunity to transform their operations and service delivery. This technology holds the potential to enhance efficiency, transparency, and citizen engagement. Governments are well-placed to spearhead efforts in advancing education and training in GenAI, thereby catalyzing the creation of new job prospects, and stimulating the growth of technology innovation hubs. These hubs will function as focal points for state-of-the-art training, bolstering skill sets, and nurturing the emergence of future AI professionals, including scientists, engineers, technicians, and specialists.

In the rapidly evolving Asia/Pacific retail market, characterized by diverse consumer preferences and advancing digital technologies, retailers are increasingly turning to GenAI to gain a competitive advantage. GenAI enables enhanced personalization, tailoring experiences to individual preferences, while also boosting efficiency by automating tasks like product design and content creation, thereby accelerating time-to-market. Furthermore, retailers leverage GenAI to create dynamic visual content and interactive experiences, fostering heightened customer engagement and loyalty.

IDCsWorldwide AI and Generative AI Spending Guidemeasures spending for technologies that analyze, organize, access, and provide advisory services based on a range of unstructured information. The Spending Guide quantifies the AI opportunity by providing data for 38 use cases across 27 industries in nine regions and 32 countries. Data is also available for the related hardware, software, and services categories. The AI and Generative AI Spending Guide is produced to provide the latest market developments through an accurate and quality forecast. During the period between updates, IDCs AI and Generative AI analyst teams conduct primary and secondary research to support this data product. Research in the period from August 2023 to February 2024 resulted in multiple additions and enhancements to the data. In this release of the AI and GenAI Spending Guide, we distilled leading forecasts such as IDCs Worldwide Black Book and IDCs Worldwide ICT Spending Guide, as well as AI and generative AI research led by IDCs AI Council of senior researchers globally.

20% of Asia/Pacific organizations are planning to build their own generative AI models. Explore IDCs latest eBook to stay equipped for the GenAI revolution. Download now:bit.ly/ genai -build-buy

**Taxonomy Note:The IDCWorldwide AI and Generative AI Spending Guideuses a precise definition of what constitutes an AI Application in which the application must have an AI component that is crucial to the application without this AI component the application will not function. This distinction enables the Spending Guide to focus on those software applications that are strongly AI-centric. In comparison, the IDCWorldwide Semiannual Artificial Intelligence Trackeruses a broad definition of AI Applications that includes applications where the AI component is non-centric, or not fundamental, to the application. This enables the inclusion of vendors that have incorporated AI capabilities into their software, but the applications are not exclusively used for AI functions. In other words, the application will function without the inclusion of the AI component.

Artificial Intelligence Amplifies State Tax Audits on High Earners – WebProNews

April 17, 2024 | Author: admin

As fears about artificial intelligence (AI) veer from job displacement to broader societal control, state tax departments harness this potent technology to boost audits on high earners significantly. Robert Frank of CNBC highlights how already high-taxed Democrate-controlled states like New York and California are increasingly deploying AI to scrutinize the tax declarations of the wealthy, intensifying efforts to reclaim unreported income.

In the past year, high-tax states have issued a surge in audit letters, with figures marking a 56% increase from the previous year. The targets? Affluent individuals who have relocated across state lines during the pandemic and remote workers whose physical locations do not align with their companys base.

AIs role in these audits is groundbreaking and unnerving for those it targets. By analyzing vast datasets, AI systems identify patterns and anomalies in tax returns more efficiently than human auditors ever could. This capability is instrumental in tracking high earners who might have underreported their incomes or falsely claimed to have moved permanently to tax-haven states.

Accountants and tax lawyers confirm that the rate of audits has escalated dramatically over the last six months. Tax authorities are challenging the permanence of moves made during the COVID-19 pandemic, insisting that many owe state taxes irrespective of their new residences. Furthermore, states are scrutinizing remote workers who, despite working entirely out-of-state, are employed by companies based in places like New York.

The fiscal implications for states are significant. With California facing a $38 billion deficit and New York bracing for a $10 billion shortfall next year, the financial incentive to pursue wealthy taxpayers is compelling. The infusion of $80 billion into the IRS, earmarked for enforcement, means that high earners are likely to face audits from both state and federal levels.

Questions linger about the efficacy and fairness of AI-driven audits. Critics ask whether these automated systems might overreach or misinterpret complex tax data, potentially leading to wrongful accusations. Yet, proponents argue that AI could revolutionize tax enforcement by uncovering hidden patterns of evasion that would be impossible for human auditors to detect.

As states and the IRS increasingly rely on artificial intelligence to bolster their audits, the landscape of tax enforcement is undergoing a profound transformation. This shift promises greater efficiency but raises important questions about privacy, fairness, and the transparency of AI algorithms in legal and financial contexts. Whether this trend will lead to a more equitable tax system or merely shift the burden more heavily onto certain groups remains to be seen.

Chamath Palihapitiya Says Voice Will Be the "Front Door" for the Next Phase of Artificial Intelligence (AI): Here Are … – The Motley Fool

April 17, 2024 | Author: admin

Among the most under-the-radar facets of artificial intelligence (AI) are its applications in voice-controlled devices.

Artificial intelligence (AI) has taken the world by storm during the past year and a half. Breakthroughs in AI are leading to sweeping changes in accelerated computing, healthcare, e-commerce, and more. It seems like the possibilities are endless, and the technology is destined to disrupt more and more areas of everyday life.

One application in the AI realm that is often overlooked is voice-recognition technology. But believe it or not, you interact with this feature of AI quite often.

Billionaire venture capitalist Chamath Palihapitiya recently took to X (the social media platform formerly known as Twitter) to predict that applications in voice will be the "front door" to the next frontier of the AI revolution.

Let's break down the market for AI-powered voice software, and explore some investment opportunities in the space.

A number of companies compete in the voice-recognition software market. Apple entered the niche through its acquisitions of Siri and Shazam. The Siri virtual assistant has become integrated throughout Apple's ecosystem and is a staple in the company's devices. Amazon has leveraged the technology in its Echo devices, and Alphabet has done the same in its Google Home smart home appliances.

On top of that, Microsoft (MSFT -0.66%) and Nvidia also entered the voice-recognition software market through a series of savvy investments.

According to Statista, the total addressable market for voice-recognition tools is forecast to reach nearly $50 billion by 2029. Considering the opportunities in this pocket of the AI landscape, it's not surprising that so many big tech enterprises are competing in it.

Image Source: Getty Images.

Two of the more prominent names in the voice-recognition market right now are SoundHound AI (SOUN -2.46%) and Microsoft.

Earlier this year, investors learned that Nvidia has a small ownership stake in SoundHound AI. After that information became public, the shares of SoundHound AI soared by as much as 320%. Although some of that momentum has waned, the company remains a top name in AI.

One of the biggest use cases for SoundHound AI's tech is voice-controlled systems in cars. With clients including Stellantis, Honda, and Hyundai, it's clear that the company has been able to attract some brand recognition.

SoundHound AI recently said that it will be offering Nvidia's DRIVE software to its automaker customers. Some applications for this technology include helping drivers answer questions related to vehicle maintenance, safety features, and car settings.

Shortly after SoundHound AI released details about its partnership with Nvidia, ChatGPT developer OpenAI made an announcement of its own. Namely, it revealed its latest product, Voice Engine, a tool that aims to help in areas such as video translation as well as in clinical settings related to speech therapy.

I agree with Palihapitiya's assertion that voice will play a big role in the further development of artificial intelligence services. In a way, it makes total sense. The sophistication of AI use cases is evolving in real time. Leveraging voice points to a future in which AI becomes even more ingrained in many aspects of daily life.

For this reason, some investors may be eager to get in on the action. As with any investment, however, there are risks.

Sure, SoundHound AI's partnership with Nvidia is exciting on the surface. But with only $46 million in revenue last year, coupled with mounting operating losses, SoundHound AI may not be the most prudent opportunity in voice-recognition technology.

Furthermore, when you layer in that Nvidia DRIVE is also being used by many other customers -- including SoundHound AI's competitor Cerence -- the potential for the partnership between the two appears less lucrative because it's not exclusive.

On the other hand, investing in Microsoft may be a subtle way to benefit from breakthroughs in AI-powered speech technology. The company is a major investor in OpenAI. Moreover, throughout 2023, Microsoft aggressively implemented ChatGPT across its Windows operating system -- a move that has unlocked a new phase of growth.

Although Voice Engine is not yet commercially available, I suspect that OpenAI will release it once it figures out how to best mitigate the risks that come with voice-mimicking technology.

Nevertheless, given Microsoft's close ties to OpenAI, I see it as a major beneficiary of voice-recognition software in the long run, and a much more proven, established opportunity in the AI narrative overall compared to other smaller competitors.

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. Adam Spatacco has positions in Alphabet, Amazon, Apple, Microsoft, and Nvidia. The Motley Fool has positions in and recommends Alphabet, Amazon, Apple, Microsoft, and Nvidia. The Motley Fool recommends Cerence and Stellantis and recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.

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Chamath Palihapitiya Says Voice Will Be the "Front Door" for the Next Phase of Artificial Intelligence (AI): Here Are ... - The Motley Fool

Artificial intelligence in liver cancer new tools for research and patient management – Nature.com

April 17, 2024 | Author: admin

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Artificial intelligence in liver cancer new tools for research and patient management - Nature.com

AI-Powered Apps Streamline Team Collaboration – PYMNTS.com

April 17, 2024 | Author: admin

Artificial intelligence (AI) chatbots are already conversing with you and are now here to enhance teamwork.

Snap, a new app by Swit Technologies, is among a wave of collaboration tools that use generative AI to streamline project management, communication and workflows. Experts say such software can include intelligent meeting schedulers, real-time document collaboration, virtual assistants, and adaptive workflow management systems.

AI can be great for speeding up or automating certain tasks and elements of collaboration that can be tedious or prone to error,Darrin Murriner, the CEO ofCloverleaf.me, told PYMNTS. In the collaboration process, this could include collaborating on documents, writing content, communicating and compiling information.

APYMNTS report from last year suggests that GenAI technologies like OpenAIs ChatGPT could significantly enhance productivity. While they may also disrupt employment landscapes, the chief operations officer at Axios HQ, Jordan Zaslav, expressed optimism about AIs role in fostering collaboration. He predicted the designation AI-powered tools might soon become as commonplace as cloud-based technologies are today, inspiring a new era of productivity.

Snap is a project management system, task manager, and message board rolled into one designed to provide a range of features that extend beyond simple conversation facilitation. The chatbot aims to support collaborative project work by offering functionalities such as converting conversations into tasks, generating checklists, offering contextual responses and summarizing tasks.

Snap is not alone in the realm of AI-powered collaboration tools. Zoom, the well-known video conferencing platform, has recently introducedZoom Workplace, an AI-driven solution aimed at boosting productivity and fostering teamwork within its user-friendly interface. The AI Companion updates feature a range of new tools, most notably Ask AI Companion, a digital assistant that helps users streamline their workday within Zoom Workplace. Other improvements include an AI Companion for Zoom Phone and enhanced capabilities for Team Chat and Whiteboard.

AI note-taking applications such as Otter.ai and Fireflies not only transcribe meeting discussions in real time but also automatically distribute these notes to all participants after the meeting,Kevin LouxofCharlotte Works told PYMNTS. This feature ensures that everyone involved has access to the same information, fostering better communication and collaboration among team members.

AI tools are definitely a booster for collaboration especially with global and remote teams,Harpaul Sambhi, CEO of the AI company Magical, told PYMNTS. By incorporating AI tools into their workflow, teams can increase productivity, improve efficiency and streamline communication. By curating a shared library of top productivity tricks from frequently used messages to common workflow automation teams work more efficiently together.

Magical uses AI to automate repetitive tasks such as messaging, Sambhi said.

With Magical, we can start to understand the common workflows of all of our users and suggest recommendations for automating those tasks, he explained. AI will help us understand those patterns. Similarly, if you think of a large organization with many employees and lots of coordination/collaboration, we can start to narrow in on the repetitive tasks of, lets say, a team or department, and start to automate the tasks between employees.

As AI evolves and as people get more comfortable with its application, the uses of AI in collaboration will evolve as well, Murriner predicted.

There will likely be a move from more routine tasks to higher-order problem-solving and solutions, as well as improving our ability to build relationships and make connections, he added. These can be useful in a multitude of ways, including improving sales performance, recommending new opportunities for collaboration, or identifying who to connect with to improve outcomes.

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Missed Out on Nvidia Stock? 1 Spectacular Artificial Intelligence (AI) Stock to Buy Instead – The Motley Fool

April 17, 2024 | Author: admin

Some investors might be upset about missing out on Nvidia's remarkable gains.

Fool.com contributor Parkev Tatevosian has identified one artificial intelligence (AI) stock that could benefit from the growing industry.

*Stock prices used were the afternoon prices of April 13, 2024. The video was published on April 15, 2024.

Parkev Tatevosian, CFA has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Nvidia. The Motley Fool has a disclosure policy. Parkev Tatevosian is an affiliate of The Motley Fool and may be compensated for promoting its services. If you choose to subscribe throughhis link, he will earn some extra money that supports his channel. His opinions remain his own and are unaffected by The Motley Fool.

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Missed Out on Nvidia Stock? 1 Spectacular Artificial Intelligence (AI) Stock to Buy Instead - The Motley Fool

Palantir Stock vs. Microsoft Stock: Which Is the Best Artificial Intelligence (AI) Stock to Buy? – The Motley Fool

April 17, 2024 | Author: admin

Palantir might be a smaller company, but that doesn't automatically make Microsoft the better investment.

Fool.com contributor Parkev Tatevosian compares Palantir Technologies (PLTR -2.60%) to Microsoft (MSFT -0.66%) to determine the better stock to buy.

*Stock prices used were the afternoon prices of April 14, 2024. The video was published on April 16, 2024.

Parkev Tatevosian, CFA has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Microsoft and Palantir Technologies. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy. Parkev Tatevosian is an affiliate of The Motley Fool and may be compensated for promoting its services. If you choose to subscribe through his link, he will earn some extra money that supports his channel. His opinions remain his own and are unaffected by The Motley Fool.

These 3 Artificial Intelligence (AI) Cryptos Are Rocketing Higher Today – Yahoo Finance

April 17, 2024 | Author: admin

It's been a wild day for cryptocurrency investors, with a number of top tokens seeing outsize volatility in today's session. For AI cryptos, these moves have been even more exaggerated.

As of 2:15 p.m. ET on Monday, The Graph (CRYPTO: GRT), Fetch.ai (CRYPTO: FET), and SingularityNET (CRYPTO:AGIX) are still up meaningfully, surging 5.6%, 2%, and 1.8%, respectively, over the past 24 hours. However, many of these tokens have continued to decline in afternoon trading alongside other risk assets, as Middle East tensions rise.

For AI cryptos, geopolitical concerns shouldn't matter to the same degree as with other assets that are more sensitive to capital flows. That said, capital flows do matter regardless of which niche a given project is pursuing, and selling pressure remains strong today.

Fetch.ai and SingularityNET are two projects uniquely focused on AI that have a shared catalyst that investors are clearly pricing in. Fetch.ai is collaborating with SingularityNET and Ocean Protocol to create what they're calling the "Superintelligence Alliance."

As part of this alliance, some talks around a potential token merger have taken place, with investors now pricing these tokens in high correlation to each other.

That certainly makes sense, given the AI focus of both projects, and their collaborative ties to work together on solving much bigger problems than they likely could on their own. One thing that certainly stands out to me about crypto assets is the relative lack of willingness for projects to merge. If these projects do tie the knot at some point, it will be interesting to see how the market values a token combination.

The demand for blockchain-based AI solutions appears to be strong, and a combination of these two relatively small-cap projects could improve their chances of success in creating meaningful utility for end users.

The Graph's core model as an oracle network, allowing off-blockchain data to be ported on-chain, has seen impressive demand build over time. A number of recent collaborations and partnerships have driven an impressive amount of momentum in this token over the past week. The fact that this momentum has continued is a very positive development for long-term investors, and suggests this AI-related play could have more room to run.

Today's price action certainly implies a dip could be on the horizon, or at least a mellowing out of some rather strong momentum in these tokens in recent days. No rally lasts forever, and a breather can turn out to be a good thing. This year, these three AI-related cryptos have been among the best performers, and I wouldn't be surprised to see that narrative carried through to the end of the year.

Story continues

For growth investors seeking some crypto exposure, (and in particular, projects with AI-related headwinds), these are three tokens that I think are worth adding to the watch list to potentially buy on dips. Each project has unique catalysts that could drive value for investors and users over time. That's what this space is supposed to be about, which is what makes assessing these cryptos so compelling.

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Chris MacDonald has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Fetch and The Graph. The Motley Fool has a disclosure policy.

These 3 Artificial Intelligence (AI) Cryptos Are Rocketing Higher Today was originally published by The Motley Fool

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These 3 Artificial Intelligence (AI) Cryptos Are Rocketing Higher Today - Yahoo Finance

Machine Learning Uncovers New Ways to Kill Bacteria With Non-Antibiotic Drugs – ScienceAlert

April 17, 2024 | Author: admin

Human history was forever changed with the discovery of antibiotics in 1928. Infectious diseases such as pneumonia, tuberculosis and sepsis were widespread and lethal until penicillin made them treatable.

Surgical procedures that once came with a high risk of infection became safer and more routine. Antibiotics marked a triumphant moment in science that transformed medical practice and saved countless lives.

But antibiotics have an inherent caveat: When overused, bacteria can evolve resistance to these drugs. The World Health Organization estimated that these superbugs caused 1.27 million deaths around the world in 2019 and will likely become an increasing threat to global public health in the coming years.

New discoveries are helping scientists face this challenge in innovative ways. Studies have found that nearly a quarter of drugs that aren't normally prescribed as antibiotics, such as medications used to treat cancer, diabetes and depression, can kill bacteria at doses typically prescribed for people.

Understanding the mechanisms underlying how certain drugs are toxic to bacteria may have far-reaching implications for medicine. If nonantibiotic drugs target bacteria in different ways from standard antibiotics, they could serve as leads in developing new antibiotics.

But if nonantibiotics kill bacteria in similar ways to known antibiotics, their prolonged use, such as in the treatment of chronic disease, might inadvertently promote antibiotic resistance.

In our recently published research, my colleagues and I developed a new machine learning method that not only identified how nonantibiotics kill bacteria but can also help find new bacterial targets for antibiotics.

Numerous scientists and physicians around the world are tackling the problem of drug resistance, including me and my colleagues in the Mitchell Lab at UMass Chan Medical School. We use the genetics of bacteria to study which mutations make bacteria more resistant or more sensitive to drugs.

When my team and I learned about the widespread antibacterial activity of nonantibiotics, we were consumed by the challenge it posed: figuring out how these drugs kill bacteria.

To answer this question, I used a genetic screening technique my colleagues recently developed to study how anticancer drugs target bacteria. This method identifies which specific genes and cellular processes change when bacteria mutate. Monitoring how these changes influence the survival of bacteria allows researchers to infer the mechanisms these drugs use to kill bacteria.

I collected and analyzed almost 2 million instances of toxicity between 200 drugs and thousands of mutant bacteria. Using a machine learning algorithm I developed to deduce similarities between different drugs, I grouped the drugs together in a network based on how they affected the mutant bacteria.

My maps clearly showed that known antibiotics were tightly grouped together by their known classes of killing mechanisms. For example, all antibiotics that target the cell wall the thick protective layer surrounding bacterial cells were grouped together and well separated from antibiotics that interfere with bacteria's DNA replication.

Intriguingly, when I added nonantibiotic drugs to my analysis, they formed separate hubs from antibiotics. This indicates that nonantibiotic and antibiotic drugs have different ways of killing bacterial cells. While these groupings don't reveal how each drug specifically kills antibiotics, they show that those clustered together likely work in similar ways.

The last piece of the puzzle whether we could find new drug targets in bacteria to kill them came from the research of my colleague Carmen Li.

She grew hundreds of generations of bacteria that were exposed to different nonantibiotic drugs normally prescribed to treat anxiety, parasite infections and cancer.

Sequencing the genomes of bacteria that evolved and adapted to the presence of these drugs allowed us to pinpoint the specific bacterial protein that triclabendazole a drug used to treat parasite infections targets to kill the bacteria. Importantly, current antibiotics don't typically target this protein.

Additionally, we found that two other nonantibiotics that used a similar mechanism as triclabendazole also target the same protein. This demonstrated the power of my drug similarity maps to identify drugs with similar killing mechanisms, even when that mechanism was yet unknown.

Our findings open multiple opportunities for researchers to study how nonantibiotic drugs work differently from standard antibiotics. Our method of mapping and testing drugs also has the potential to address a critical bottleneck in developing antibiotics.

Searching for new antibiotics typically involves sinking considerable resources into screening thousands of chemicals that kill bacteria and figuring out how they work. Most of these chemicals are found to work similarly to existing antibiotics and are discarded.

Our work shows that combining genetic screening with machine learning can help uncover the chemical needle in the haystack that can kill bacteria in ways researchers haven't used before.

There are different ways to kill bacteria we haven't exploited yet, and there are still roads we can take to fight the threat of bacterial infections and antibiotic resistance.

Mariana Noto Guillen, Ph.D. Candidate in Systems Biology, UMass Chan Medical School

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Machine Learning Uncovers New Ways to Kill Bacteria With Non-Antibiotic Drugs - ScienceAlert

Machine learning reveals the control mechanics of an insect wing hinge – Nature.com

April 17, 2024 | Author: admin

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Machine Learning Uncovers New Ways to Kill Bacteria With Non-Antibiotic Drugs – ScienceAlert

Machine learning reveals the control mechanics of an insect wing hinge – Nature.com

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