Since Shein became the world's most popular online shopping destination--with seemingly unbeatable prices, and influencers posting "haul" videos to show off their purchases on social media--the Chinese fast-fashion giant has raised questions over how it produces its plethora of merchandise at dizzying speeds. The answer: AI-powered algorithms that allow the company to pick up changes in customer demand and interest, allowing it to adjust its supply chain in real time. As a result, Shein reportedly lists as many as 600,000 items on its online platform at any given moment, selling to customers in over 220 countries and regions globally. But the company has also long been under scrutiny for its poor record on environmental sustainability, becoming fashion's biggest polluter in 2023. Investigations into Shein's supply chains have found severe labor rights violations, with factory workers in Southern Chinese manufacturing plants reporting grueling 75-hour work weeks to keep up with demand. Shein claims AI is the answer to solving these problems, too.

The Bible maintains that "the race is not to the swift, nor the battle to the strong", but, as Damon Runyon used to say, "that is the way to bet". As a species, we take the same view, which is why we are obsessed with "races". Political journalism, for example, is mostly horserace coverage – runners and riders, favourites, outsiders, each-way bets, etc. And when we get into geopolitics and international relations we find a field obsessed with arms "races". In recent times, a new kind of weaponry – loosely called "AI" – has entered the race.

Machine learning and artificial intelligence are two of the most important technological advances in recent times. Machine learning is a field that promises to disrupt (to borrow a favorite term) our lives as we know them. However, many of its applications are still unexplored. See the example of machine learning in action! These are 10 companies using machine learning in exciting new ways. It's easier these days to list areas in scientific R&D that Google (or, rather, parent company Alphabet-) isn't working upon, than to try to summarize Google's technological ambitions.

The problem with banking's use of artificial intelligence technology is not always in a bank's inability or unwillingness to invest in it. In fact, more financial institutions are spending on AI and related technologies as banking leaders strive to build out customer personalization. The problem instead can lie in a financial institution's failure to align the technology with the bank's strategy. As a result, a bank or credit union might buy (or build) the technology, but then abandon it shortly after or fail to follow through with it, a McKinsey report points out. The report uses an unnamed large retail bank as an example, one that has set aside resources in the budget for machine-learning (ML) technology -- one part of AI -- to automate marketing and customer personalization campaigns.

How much is too much? These are questions that cut to the heart of a complex issue currently preoccupying senior medical physicists when it comes to the training and continuing professional development (CPD) of the radiotherapy physics workforce. What's exercising management and educators specifically is the extent to which the core expertise and domain knowledge of radiotherapy physicists should evolve to reflect – and, in so doing, best support – the relentless progress of artificial intelligence (AI) and machine-learning technologies within the radiation oncology workflow. In an effort to bring a degree of clarity and consensus to the collective conversation, the ESTRO 2022 Annual Congress in Copenhagen last month featured a dedicated workshop session entitled "Every radiotherapy physicist should know about AI/machine learning…but how much?" With several hundred delegates packed into Room D5 at the Bella Center, speakers were tasked by the session moderators with defending a range of "optimum scenarios" to align the know-how of medical physicists versus emerging AI/machine-learning opportunities in the radiotherapy clinic.

While chatbots and self-driving cars are some of the most widely recognised applications for artificial intelligence (AI), machine-learning technologies have made a decisive mark in the scientific world. Read on to discover more about scientific AI and the implications it has on everything from genomics to drug development. Technological advances have allowed scientists and researchers to unlock extraordinary amounts of data. Though without enough processing power, this data is meaningless. This is where artificial intelligence steps up.

While chatbots and self-driving cars are some of the most widely recognised applications for artificial intelligence (AI), machine-learning technologies have made a decisive mark in the scientific world. Read on to discover more about scientific AI and the implications it has on everything from genomics to drug development. Technological advances have allowed scientists and researchers to unlock extraordinary amounts of data. Though without enough processing power, this data is meaningless. This is where artificial intelligence steps up.

Powerful algorithms used by Netflix, Amazon and Facebook can'predict' the biological language of cancer and neurodegenerative diseases like Alzheimer's, scientists have found. Big data produced during decades of research was fed into a computer language model to see if artificial intelligence can make more advanced discoveries than humans. Academics based at St John's College, University of Cambridge, found the machine-learning technology could decipher the'biological language' of cancer, Alzheimer's, and other neurodegenerative diseases. Their ground-breaking study has been published in the scientific journal PNAS today (April 8 2021) and could be used in the future to'correct the grammatical mistakes inside cells that cause disease'. Professor Tuomas Knowles, lead author of the paper and a Fellow at St John's College, said: "Bringing machine-learning technology into research into neurodegenerative diseases and cancer is an absolute game-changer. Ultimately, the aim will be to use artificial intelligence to develop targeted drugs to dramatically ease symptoms or to prevent dementia happening at all."

Algorithms similar to those used by Netflix, Amazon and Facebook have shown the ability to decipher the'biological language' of cancer, Alzheimer's and other neurodegenerative diseases. Researchers trained a large-scale language model with a recommendation AI to look at what happens when something goes wrong with proteins that leads to the development of a disease. The work, conducted by St. John's College and the University of Cambridge, programed the algorithm to learn the language of shapeshifting droplets of proteins found in cells in order to understand their function and malfunction. By learning these protein droplets' language, the team can then'correct the grammatical mistakes inside cells that cause disease.'' Professor Tuomas Knowles, a Fellow at St John's College, said: 'Any defects connected with these protein droplets can lead to diseases such as cancer. 'This is why bringing natural language processing technology into research into the molecular origins of protein malfunction is vital if we want to be able to correct the grammatical mistakes inside cells that cause disease.' Machine learning technology has made waves in the tech industry – Netflix uses it to recommend series, Facebook's suggest someone to friend and Amazon's Alexa has an algorithm to recognize people based on their voice.

Powerful algorithms used by Netflix, Amazon and Facebook can'predict' the biological language of cancer and neurodegenerative diseases like Alzheimer's, scientists have found. Big data produced during decades of research was fed into a computer language model to see if artificial intelligence can make more advanced discoveries than humans. Academics based at St John's College, University of Cambridge, found the machine-learning technology could decipher the'biological language' of cancer, Alzheimer's, and other neurodegenerative diseases. Their ground-breaking study has been published in the scientific journal PNAS today and could be used in the future to'correct the grammatical mistakes inside cells that cause disease'. Professor Tuomas Knowles, lead author of the paper and a Fellow at St John's College, said: "Bringing machine-learning technology into research into neurodegenerative diseases and cancer is an absolute game-changer. Ultimately, the aim will be to use artificial intelligence to develop targeted drugs to dramatically ease symptoms or to prevent dementia happening at all."