A recent study published in The Lancet Oncology suggests that artificial intelligence (AI) could be as effective as two human radiologists when it comes to reviewing breast cancer scans. The trial involved 80,000 women between the ages of 40 and 80, with an average age of 54 years old.

During the trial, a combination of AI detection and human radiologists conducting reviews was used to screen 39,996 patients, while the standard process of two human radiologists was used for the remaining 40,024 patients. The AI review detected 41 more cases of breast cancer compared to the radiologists, with 19 classified as invasive and 22 as in situ cancers.

However, the study also raised concerns about potential overdiagnosis, as AI-supported screening found 60 in situ cancers compared to the 38 found in standard screening. On the positive side, the AI-supported screening did not result in more false-positive results than standard screening, with an average false-positive rate of 1.5% in both groups.

In addition to its detection capabilities, AI could significantly reduce radiologists workload. The study estimates that incorporating AI into the screening process could reduce the need for two radiologists to review mammograms to just one. This becomes crucial as the field of radiology is expected to face a shortage of professionals by 2024.

Lead author Kristina Lng, Ph.D., emphasized that more research is needed before AI can be fully implemented in mammography screening. Further trials and evaluations are necessary to assess the technologys effectiveness, cost-efficiency, and the extent to which it can assist radiologists.

While the trial is ongoing, the potential impact of AI in reducing the number of missed breast cancer cases and improving efficiency in screenings is being closely monitored. Breast cancer is the most prevalent cancer worldwide, underscoring the importance of early detection and treatment.

In conclusion, the trial results indicate the promise of AI in breast cancer screening, suggesting that it has the potential to enhance detection rates and reduce radiologists workload. However, further research and evaluation are required before widespread implementation can occur.

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Artificial Intelligence Shows Promise in Breast Cancer Screening - Fagen wasanni

Who are the earliest adopters of new technologies? Cutting-edge stuff tends to be expensive, meaning the answer is often the extremely rich. Early adopters also tend to be incentivised by cut-throat competition to look beyond the status quo. As such, there may be no group more likely to pick up new tools than the uber-rich and hyper-competitive hedge-fund industry.

This rule appears to hold for artificial intelligence (ai) and machine learning, which were first employed by hedge funds decades ago, well before the recent hype. First came the quants, or quantitative investors, who use data and algorithms to pick stocks and place short-term bets on which assets will rise and fall. Two Sigma, a quant fund in New York, has been experimenting with these techniques since its founding in 2001. Man Group, a British outfit with a big quant arm, launched its first machine-learning fund in 2014. AQR Capital Management, from Greenwich, Connecticut, began using ai at around the same time. Then came the rest of the industry. The hedge funds experience demonstrates ais ability to revolutionise businessbut also shows that it takes time to do so, and that progress can be interrupted.

Ai and machine-learning funds seemed like the final step in the march of the robots. Cheap index funds, with stocks picked by algorithms, had already swelled in size, with assets under management eclipsing those of traditional active funds in 2019. Exchange-traded funds offered cheap exposure to basic strategies, such as picking growth stocks, with little need for human involvement. The flagship fund of Renaissance Technologies, the first ever quant outfit, established in 1982, earned average annual returns of 66% for decades. In the 2000s fast cables gave rise to high-frequency market makers, including Citadel Securities and Virtu, which were able to trade shares by the nanosecond. Newer quant outfits, like AQR and Two Sigma, beat humans returns and gobbled up assets.

By the end of 2019, automated algorithms took both sides of trades; more often than not high-frequency traders faced off against quant investors, who had automated their investment processes; algorithms managed a majority of investors assets in passive index funds; and all of the biggest, most successful hedge funds used quantitative methods, at least to some degree. The traditional types were throwing in the towel. Philippe Jabre, a star investor, blamed computerised models that had imperceptibly replaced traditional actors when he closed his fund in 2018. As a result of all this automation, the stock market was more efficient than ever before. The execution was lightning fast and cost next to nothing. Individuals could invest savings for a fraction of a penny on the dollar.

Machine learning held the promise of still greater fruits. The way one investor described it was that quantitative investing started with a hypothesis: that of momentum, or the idea that stocks which have risen faster than the rest of the index would continue to do so. This hypothesis allows individual stocks to be tested against historical data to assess if their value will continue to rise. By contrast, with machine learning, investors could start with the data and look for a hypothesis. In other words, the algorithms could decide both what to pick and why to pick it.

Yet automations great march forward has not continued unabatedhumans have fought back. Towards the end of 2019 all the major retail brokers, including Charles Schwab, E-trade and TD, Ameritrade, slashed commissions to zero in the face of competition from a new entrant, Robinhood. A few months later, spurred by pandemic boredom and stimulus cheques, retail trading began to spike. It reached a peak in the frenzied early months of 2021 when day traders, co-ordinating on social media, piled into unloved stocks, causing their prices to spiral higher. At the same time, many quantitative strategies seemed to stall. Most quants underperformed the markets, as well as human hedge funds, in 2020 and early 2021. AQR closed a handful of funds after persistent outflows.

When markets reversed in 2022, many of these trends flipped. Retails share of trading fell back as losses piled up. The quants came back with a vengeance. AQR's longest-running fund returned a whopping 44%, even as markets shed 20%.

This zigzag, and robots growing role, holds lessons for other industries. The first is that humans can react in unexpected ways to new technology. The falling cost of trade execution seemed to empower investing machinesuntil costs went to zero, at which point it fuelled a retail renaissance. Even if retails share of trading is not at its peak, it remains elevated compared with before 2019. Retail trades now make up a third of trading volumes in stocks (excluding market makers). Their dominance of stock options, a type of derivative bet on shares, is even greater.

The second is that not all technologies make markets more efficient. One of the explanations for Aqrs period of underperformance, argues Cliff Asness, the firms co-founder, is how extreme valuations became and how long a bubble in everything persisted. In part this might be the result of overexuberance among retail investors. Getting information and getting it quickly does not mean processing it well, reckons Mr Asness. I tend to think things like social media make the market less, not more, efficient...People dont hear counter-opinions, they hear their own, and in politics that can lead to some dangerous craziness and in markets that can lead to some really weird price action.

The third is that robots take time to find their place. Machine-learning funds have been around for a while and appear to outperform human competitors, at least a little. But they have not amassed vast assets, in part because they are a hard sell. After all, few people understand the risks involved. Those who have devoted their careers to machine learning are acutely aware of this. In order to build confidence, we have invested a lot more in explaining to clients why we think the machine-learning strategies are doing what they are doing, reports Greg Bond of Man Numeric, Man Groups quantitative arm.

There was a time when everyone thought the quants had figured it out. That is not the perception today. When it comes to the stock market, at least, automation has not been the winner-takes-all event that many fear elsewhere. It is more like a tug-of-war between humans and machines. And though the machines are winning, humans have not let go just yet.

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2023, The Economist Newspaper Limited. All rights reserved. From The Economist, published under licence. The original content can be found on http://www.economist.com

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Lessons from finances experience with artificial intelligence - Hindustan Times

With innovation comes growth, investment opportunity, and progress. However, it also brings along growing pains and fears. Just like the advent of the internet in the 1990s, artificial intelligence (AI) is one of the most polarizing technologies of 2023.

The potentially limitless applications of AI, particularly as an investing tool, have raised concerns among our readers. They wonder whether AI is safe or if its negative impacts on society will outweigh the incredible good it has already done for the U.S. economy.

The buzz this year revolves around defining AI itself. When a new technology emerges, there are always different perspectives. Some view it with optimism and enthusiasm (the Baptists), while others approach it with caution and skepticism (the Bootleggers).

One crucial aspect to consider is the impact of AI on total factor productivity growth, which is vital for the economy. AI has been instrumental in enhancing productivity and efficiency across various industries. It is revolutionizing sectors like healthcare, finance, transportation, and many others.

Despite the concerns about the safety of AI, it is important to strike a balance between acknowledging its potential benefits and addressing potential risks. Implementing proper regulations and ethical frameworks can help ensure the responsible development and deployment of AI technology.

While there are valid concerns surrounding AI, it is essential to recognize the transformative power it holds. AI has the potential to improve lives, drive economic growth, and reshape industries. By embracing AI while being mindful of its ethical implications, we can maximize its positive impact while minimizing any negative consequences.

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The Safety of Artificial Intelligence: Balancing Potential and Concerns - Fagen wasanni

SINGAPORE The development of personalised cancer treatments will get a boost from an artificial intelligence-based method that can identify cancer mutations in DNA fragments inside tumour samples.

The method, called Variant Network (VarNet), uses deep learning to detect cancer mutations. It is developed by scientists from the Genome Institute of Singapore(GIS), a research institute under the Agency for Science, Technology and Research (A*Star).

Cancer is generally thought to be caused by mutations in our genomes, and its essential to identify these mutations to tailor the most effective treatment for the individual patients, said Dr Anders Skanderup, group leader of GIS Laboratory of Computational Cancer Genomics.

In line with the precision medicine approach where medical treatment is tailored to the individual based on factors such as variations in genetics and environment drugs prescribed for cancer treatment increasingly work only when certain mutations are present, he said.

A high level of accuracy is needed when identifying cancer mutations, he added.

VarNet is a mutation caller, which identifies mutations by sifting through raw DNA sequencing data.

Using artificial intelligence (AI), VarNet is trained to identify mutations through exposure to millions of real cancer mutations as well as to examples of false cancer mutations.

This enables VarNet to detect real mutations while ignoring false ones, Dr Skanderup told The Straits Times.

A paper published in the peer-reviewed scientific journal Nature Communications in July 2022 found VarNet often exceeded existing mutation identification algorithms in terms of accuracy.

While other AI-based methods of detecting cancer mutations exist, these rely heavily on human experts providing vast amounts of detailed training data to the models to train them to identify mutations, he said.

Deep learning an AI method where computers are taught to process data in a way that mimics the human brain allows VarNet to distinguish between real and false mutations, essentially teaching itself the rules of doing so, with minimal human intervention.

The papers first author Kiran Krishnamachari an A*Star Computing and Information Science scholar affiliated with GIS noted VarNet is able to learn to detect mutations from the raw data in a manner that a human expert would do when manually inspecting potential mutations.

This gives us the confidence that the system can learn relevant mutational features when trained on vast sequencing datasets, using our weak-supervision strategy that does not require excessive manual labelling, he said.

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Artificial intelligence developed to identify cancer mutations - The Straits Times