Machine learning computer systems, which get better with experience, are poised to transform the economy much as steam engines and electricity have in the past. They can outperform people in a number of tasks, though they are unlikely to replace people in all jobs.
So say Carnegie Mellon University’s Tom Mitchell and The Massachusetts Institute of Technology’s Erik Brynjolfsson in a Policy Forum commentary published in the Dec. 22 edition of the journal Science. Mitchell, who founded the world’s first Machine Learning Department at CMU, and Brynjolfsson, director of the MIT Initiative on the Digital Economy in the Sloan School of Management, describe 21 criteria to evaluate whether a task or a job is amenable to machine learning (ML).
“Although the economic effects of ML are relatively limited today, and we are not facing the imminent ‘end of work’ as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound,” they write. The skills people choose to develop and the investments businesses make will determine who thrives and who falters once ML is ingrained in everyday life, they argue.
ML is one element of what is known as artificial intelligence. Rapid advances in ML have yielded recent improvements in facial recognition, natural language understanding and computer vision. It already is widely used for credit card fraud detection, recommendation systems and financial market analysis, with new applications such as medical diagnosis on the horizon.
Predicting how ML will affect a particular job or profession can be difficult because ML tends to automate or semi-automate individual tasks, but jobs often involve multiple tasks, only some of which are amenable to ML approaches.
“We don’t know how all of this will play out,” acknowledged Mitchell, the E. Fredkin University Professor in CMU’s School of Computer Science. Earlier this year, for instance, researchers showed that a ML program could detect skin cancers better than a dermatologist. That doesn’t mean ML will replace dermatologists, who do many things other than evaluate lesions.
“I think what’s going to happen to dermatologists is they will become better dermatologists and will have more time to spend with patients,” Mitchell said. “People whose jobs involve human-to-human interaction are going to be more valuable because they can’t be automated.”
Tasks that are amenable to ML include those for which a lot of data is available, Mitchell and Brynjolfsson write. To learn how to detect skin cancer, for instance, ML programs were able to study more than 130,000 labeled examples of skin lesions. Likewise, credit card fraud detection programs can be trained with hundreds of millions of examples.
ML can be a game changer for tasks that already are online, such as scheduling. Jobs that don’t require dexterity, physical skills or mobility also are more suitable for ML. Tasks that involve making quick decisions based on data are a good fit for ML programs; not so if the decision depends on long chains of reasoning, diverse background knowledge or common sense.
ML is not a good option if the user needs a detailed explanation for how a decision was made, according to the authors. In other words, ML might be better than a physician at detecting skin cancers, but a dermatologist is better at explaining why a lesion is cancerous or not. Work is underway, however, on “explainable” ML systems.
Understanding the precise applicability of ML in the workforce is critical for understanding its likely economic impact, the authors say. Earlier this year, a National Academies of Sciences, Engineering and Medicine study on information technology and the workforce, co-chaired by Mitchell and Brynjolfsson, noted that information technology advances have contributed to growing wage inequality.
“Although there are many forces contributing to inequality, such as increased globalization, the potential for large and rapid changes due to ML, in many cases within a decade, suggests that the economic effects may be highly disruptive, creating both winners and losers,” they write. “This will require considerable attention among policy makers, business leaders, technologists and researchers.”
Learn more: Machine Learning Will Change Jobs
The Latest on: Machine learning
[google_news title=”” keyword=”Machine learning” num_posts=”10″ blurb_length=”0″ show_thumb=”left”]- From Mad Men to Machine Learning: The AI fuelled marketing revolutionon April 28, 2024 at 3:01 am
The reality of AI, thankfully, is far more nuanced and beneficial. While a 2023 US survey found that nearly half of consumers lack a clear understanding of AI and machine learning, its influence is ...
- ICMR-NIRRCH To Hold Workshop On Applications of Artificial Intelligence and Machine Learning in Disease Informatics, Detailson April 27, 2024 at 8:30 pm
New Delhi- The Indian Council of Medical Research (ICMR) and National Institute for Research in Reproductive and Child Health (NIRRCH) are going to organise a workshop on “Applications ...
- Automated machine learning robot unlocks new potential for genetics researchon April 26, 2024 at 9:10 am
University of Minnesota Twin Cities researchers have constructed a robot that uses machine learning to fully automate a complicated microinjection process used in genetic research.
- 3 Machine Learning Stocks That Could Triple Your Money by 2030on April 26, 2024 at 3:58 am
InvestorPlace - Stock Market News, Stock Advice & Trading Tips Investing in machine learning (ML) stocks presents an enticing opportunity for ...
- The Top 3 Machine Learning Stocks to Buy in April 2024on April 26, 2024 at 3:15 am
InvestorPlace - Stock Market News, Stock Advice & Trading Tips The U.S. economy is poised for remarkable growth driven by advancements in ...
- Machine learning model predicts CIS to MS conversion risk: Studyon April 25, 2024 at 10:00 pm
A machine learning model can predict the risk of converting from clinically isolated syndrome (CIS) to multiple sclerosis (MS), per a study.
- Artificial intelligence, machine learning boost animal science researchon April 25, 2024 at 10:44 am
Science X is a network of high quality websites with most complete and comprehensive daily coverage of the full sweep of science, technology, and medicine news ...
- Machine learning and extended reality used to train welderson April 25, 2024 at 10:30 am
Ever since the ancient Egyptians hammered two pieces of gold together until they fused, the art of welding has continuously progressed.
- JFrog unveils MLflow integration to enhance machine learning model managementon April 25, 2024 at 6:15 am
Software supply chain company JFrog Ltd. today announced a new machine learning lifecycle integration between JFrog Artifactory and MLflow, an open-source software platform origin ...
- Machine learning and experimenton April 25, 2024 at 5:31 am
For more than 20 years in experimental particle physics and astrophysics, machine learning has been accelerating the pace of science, helping scientists tackle problems of greater and greater ...
via Google News and Bing News