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The brain is a generator of automatisms, which allow us to do things even though we cant explain how we do them. The goalkeeper who dives to clear that ball in the corner, the gymnast who throws the ribbon and catches it without looking after several somersaults, the tennis player who connects the passing shot on the run. None of them think about (or know), while executing these movements, the mathematical model or the laws of physics that determine these trajectories, yet nevertheless, based on some basic concepts and millions of repetitions, they are capable of doing them.

But sometimes, something happens that truncates that ability. Like Simone Biles at the Tokyo Olympics, sometimes the brain loses its automatisms. The gymnasts lose their axis, the golfers their swing, the tennis players their serve. The concepts have not been forgotten, but the automatisms fail. And if you have to think, it no longer works. To recover, they have to slowly rebuild their automatisms, until they are able, once again, to play without thinking.

Computers, unlike the brain, need explicit models. In order to send a rocket to the Moon, complex trajectories are designed with high precision. And to study the effect of an economic policy measure, a mathematical model is designed that simulates the functioning of the economy. Computers need instructions, they do not know how to generate automatisms. Thats how they differ from humans.

Artificial intelligence started like this, giving instructions to the computer. To translate a document, a model was designed that replicated the grammar of the language. To play chess, a program was designed that replicated the rules of the game. But it soon became clear this road was very limited. How do you write a program to teach a computer what a cat is? Or to detect a tumor on an X-ray? Human intelligence is different, it does not work with models. A baby is not taught to recognize the face of her parents. But after a few days, by dint of seeing them, she is able to do it.

Data is also the limit of artificial intelligence, because the power of an algorithm is limited to its database. Thats why artificial intelligence replaces tasks, not jobs or business strategies

Thats because the brain is a machine for predicting the immediate present, based on trial and error. Each action and its consequence generate a neural connection, each repetition of that action reinforces that neural connection, and based on repetitions the connection is consolidated and the brain learns.

Artificial intelligence has evolved towards the prediction of the present. The immense improvement in the processing capacity of computers, and the exponential increase in the data available for analysis more than 90 percent of the data available today has been created in recent years makes it possible for computers to operate in a similar way to the brain.

Text translation is done based on the analysis of millions of translations, and the computer learns to predict which word or phrase in one language is related to another in another language. Facial recognition takes advantage of the digitization and tagging of millions of photos, which enables relational analysis of images. Autonomous driving systems are built with the digitization and analysis of the actions of human drivers, to be able to predict and replicate their behavior. Any activity that can be digitized and tagged can be turned into a prediction exercise, and therefore automated.

Artificial intelligence reaches the most unexpected corners. For example, this summer I witnessed how, in one of the most famous Rioja wineries in Spain, harvested grapes that are in poor condition are no longer discarded manually, but with an artificial intelligence system: the computer has been trained to recognize images of grapes in poor condition, the cameras detect them on the conveyor belt and activate a system of pressurized air jets that eliminates them before reaching the pressing barrel.

Computers perform arithmetic and probability analysis better than humans, but humans are superior in value judgments and intangible decisions

The economics of artificial intelligence is the economics of prediction. The computer reduced the cost of arithmetic operations, making the prediction process cheaper. The improvement of internet connections exponentially expanded the volume of data available to apply this arithmetic. The combination of more powerful computers and faster internet connections makes the system globally scalable, making prediction infinitely cheaper and more accurate, allowing many activities to be converted into prediction exercises.

Data, be it images, videos, or texts, is the raw material of artificial intelligence, the fundamental element for learning and training algorithms. Every time you send a message or upload a photo to the internet you are helping develop or improve artificial intelligence algorithms. The famous cookies, and internet searches, capture patterns of digital behavior that will serve as training for algorithms. Data regulation is not just a matter of privacy, but also of ownership of this fundamental raw material.

Data, in the world of statistics and econometrics, shows diminishing returns: once a model is estimated, one more data point does not materially improve its prediction ability. But in the world of artificial intelligence, data shows increasing returns: with little data you cannot do facial recognition, or autonomous driving systems. But the accumulation of data at some point makes it possible and economically viable, and from there the improvements are exponential. This explains the interest of technology companies in companies that, although not profitable, are generators of data. The exclusivity of the data, more than the details of the algorithms, is the key to success in artificial intelligence.

Data is also the limit of artificial intelligence, because the power of an algorithm is limited to its database. Thats why artificial intelligence replaces tasks, not jobs or business strategies. The key to technological progress is the combination of machines and humans. The best chess players are not humans, nor computers, but humans with the help of computers. Computers perform arithmetic and probability analysis better than humans, but humans are superior in value judgments and intangible decisions, because the accumulated experience in their brains their database is far superior in quantity and especially in diversity to that of computers. And that allows them to react to an unforeseen event for which the algorithm was not trained. It also facilitates creativity, which almost always springs from interdisciplinary connections think of molecular cuisine, for example. Therefore, it is essential to educate citizens so that they know how to operate with computers computing should be as mandatory as a second language but without forgetting the humanistic subjects and abstract reasoning that provide that agility and creative advantage.

Technological progress is the source of growth and, therefore, of job creation. But you have to be well prepared to take advantage of it.

Twitter: @angelubide

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The economics of artificial intelligence - EL PAS in English

Quick, before we reach the singularity! This week, the UN released a report on the state of Artificial Intelligence (AI), and in a nutshell, theyre not liking what they see. The report comes from UN High Commissioner for Human Rights Michelle Bachelet, and it doesnt mince words. Lets check it out!

Basically, the UN has found that both private companies and states/countries themselves are using AI technology that violates international human rights laws. Specifically, theyre worried that AI-based profiling, automated decision-making, and other machine-learning technologies can have disastrous consequences for people.

In addition to violating privacy laws, these technologies can affect a persons rights to health, education, freedom of movement, freedom of peaceful assembly and association, and freedom of expression. So yeah, not a great way to be using our newfound tech.

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#AI incl. profiling, automated decision-making & machine-learning affects peoples right to privacy and other rights, such as rights to health, education, freedom of movement, freedom of peaceful assembly & association, and freedom of expression. https://t.co/VmmR75aKzD pic.twitter.com/Xs9zzFGIbs

UN Human Rights (@UNHumanRights) September 15, 2021

Some specific examples of these issues include getting denied social security benefits due to faulty AIor even being arrested thanks to flawed facial recognition tools. Yeah, this is starting to sound more and more like a sci-fi movie, but these are legitimate problems.

Well use targeted ads as a quick example of how things can go wrong with Big Data and AI. As youre browsing around the internet, your interests and activity are tracked and accumulated by social media companies, advertisers, and whoever else has the cash to access it. Boom, two weeks later, and that thing you thought you needed (or maybe even didnt) is right there, waiting for you to buy it.

Now, thats not really a problem in and of itself, but were going to continue the analogy. Lets say you were browsing the internet looking for gifts for friends, researching a school project, or whatever else. Well then, the AI cant distinguish your intent from your behaviour it takes it at face value. All of a sudden, your friend sees you scrolling through Instagram and their birthday present is the first sponsored ad.

Shopping is one thing, but political actions, personal health decisions, and other deeply important behaviour go down online as well. And as long as AI operates indiscriminately and without oversight, the risks for mistakes with grave consequences continue to grow.

The UN has seen this, and theyre (justifiably) freaked out about it. While we cant see the worlds leading countries or international corporations taking their advice any time soon, were happy theyve said something about it. If youd like to check out the report for yourself, just click here.

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The UN wants the world to pump the brakes on Artificial Intelligence - Curiocity

On 14 September 2021, eight international organisations joined forces to launch a new portal promoting global co-operation on artificial intelligence (AI). The portal is a one-stop shop for data, research findings and good practices in AI policy.

The objective of the portal is to help policymakers and the wider public navigate the international AI governance landscape. It provides access to the necessary tools and information, such as projects, research and reports to promote trustworthy and responsible AI that is aligned with human rights at global, national and local level.

Key partners in this joint effort include the Council of Europe, the European Commission, the European Union Agency for Fundamental Rights, the Inter-American Development Bank, the Organisation for Economic Co-operation and Development (OECD), the United Nations (UN), the United Nations Educational, Scientific and Cultural Organization (UNESCO), and the World Bank Group.

Access the website: https://globalpolicy.ai

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Artificial Intelligence A New Portal to Promote Global Cooperation Launched with 8 International Organisations - Council of Europe

Artificial intelligence (AI) is sometimes referred to as a herald of the fourth industrial revolution. That revolution is already here. Whenever you say Hey Siri or glance at your phone in order to unlock it, youre using AI. Its current and potential applications are numerous, including medical diagnosis and predictive technologies that enhance user interactions.

As chairwoman of the U.S. House Committee on Science, Space, and Technology, I am particularly interested in the potential for AI to accelerate innovation and discovery across the science and engineering disciplines. Just last year, DeepMind announced that its AI system AlphaFold had solved a protein-folding challenge that had stumped biologists for half a century. It is clear that not only will AI technologies be integral to improving the lives of Americans, but they will also help determine Americas standing in the world in the decades to come.

However, the vision of AIs role in humanitys future isnt all rosy. Increasingly autonomous devices and growing amounts of data will exacerbate traditional concerns, such as privacy and cybersecurity.Other potential dangers of AI have also arrived, appearing as patterns of algorithmic bias that often reflect our societys systemic racial and gender-based biases. We have seen discriminatory outcomes in AI systems that predict credit scores, health care risks, and recruitment potential. These are domains where we must mitigate the risk of bias in our decision-making, and the tools we use to augment that decision-making.

Technological progress does not have to come at the expense of safety, security, fairness, or transparency. In fact, embedding our values into technological development is central to our economic competitiveness and national security. Our federal government has the responsibility to work with private industry to ensure that we are able to maximize the benefits of AI technology for society while simultaneously managing its emerging risks.

To this end, the Science Committee has engaged in efforts to promote trustworthy AI. Last year, one of our signature achievements was passing the bipartisan National Artificial Intelligence Initiative Act, which directs the Department of Commerces National Institute of Standards and Technology (NIST) to develop a process for managing AI risks.

NIST may not be the most well-known government institution, but it has long conducted critical work on standard-setting and measurement research that is used by federal agencies and private industry. Over the past year, NIST has conducted a series of workshops examining topics like AI trustworthiness, bias, explainability, and evaluation. These workshops are geared at helping industry professionals understand how to detect, catalogue, and ultimately prevent the harmful outcomes that erode public trust in AI technology.

Most recently, NIST has been working to construct a voluntary Risk Management Framework that is intended to support the development and deployment of safe and trustworthy AI. This framework will be important for informing the work of both public and private sector AI researchers as they pursue their game-changing research. NIST is soliciting public comments until Sept. 15, 2021 and will develop the framework in several iterations, allowing for continued input. Interested stakeholders should submit comments and/or participate in the ongoing processes at NIST.

We know that AI has the potential to benefit society and make the world a better place. In order for the U.S. to be a true global leader in this technology, we have to ensure that the AI we create does just that.

Eddie Bernice JohnsonEddie Bernice JohnsonUS must not only lead in artificial intelligence, but also in its ethical application Our approach to schizophrenia is failing House passes bills to boost science competitiveness with China MORE represents the 30th District of Texas and is chairwoman of the House Committee on Science, Space, and Technology.

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US must not only lead in artificial intelligence, but also in its ethical application | TheHill - The Hill

Yan Cui, PhD, associate professor in the UTHSCDepartment of Genetics, Genomics, and Informatics,recently received a $1.7 million grant from the National Cancer Institute for a study titled Algorithm-based prevention and reduction of cancer health disparity arising from data inequality.

Dr. Cuis project aims to prevent and reduce health disparities caused by ethnically-biased data in cancer-related genomic and clinical omics studies. His objective is to establish a new machine learning paradigm for use with multiethnic clinical omics data.

For nearly 20 years, scientists have been using genome-wide association studies, known as GWAS, and clinical omics studies to detect the molecular basis of diseases. But statistics show that over 80% percent of data used in GWAS come from people of predominantly European descent.

As artificial intelligence (AI) is increasingly applied to biomedical research and clinical decisions, this European-centric skew is set to exacerbate long-standing disparities in health. With less than 20% of genomic samples coming from people of non-European descent, underrepresented populations are at a severe disadvantage in data-driven, algorithm-based biomedical research and health care.

Biomedical data-disadvantage has become a significant health risk for the vast majority of the worlds population, Dr. Cui said. AI-powered precision medicine is set to be less precise for the data-disadvantaged populations including all the ethnic minority groups in the U.S. We are committed to addressing the health disparities arising from data inequality.

The project is innovative in the type of machine learning technique it will use. Multiethnic machine learning normally uses mixture learning and independent learning schemes. Dr. Cuis project will instead be using a transfer learning process.

Transfer learning works much the same way as human learning. When faced with a new task, instead of starting the learning process from scratch, the algorithm leverages patterns learned from solving a related task. This approach greatly reduces the resources and amount of data required for developing new models.

Using large-scale cancer clinical omics data and genotype-phenotype data, Dr. Cuis lab will examine how and to what extent transfer learning improves machine learning on data-disadvantaged cohorts. In tandem with this, the team aims to create an open resource system for unbiased multiethnic machine learning to prevent or reduce new health disparities.

Neil Hayes, MD, MPH, assistant dean for Cancer Reesearch in the UTHSC College of Medicine and director of the UTHSC Center for Cancer Research, and Athena Starlard-Davenport, PhD, associate professor in the Department of Genetics, Genomics, and Informatics, are co-Investigators on the grant. Yan Gao, PhD, a postdoctoral scholar working with Dr. Cui, is a machine learning expert in the team. A pilot study for this project, funded by the UT Center for Integrative and Translational Genomics and UTHSC Office of Research, has been published in Nature Communications.

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Yan Cui and Team Are Innovating Artificial Intelligence Approach to Address Biomedical Data Inequality - UTHSC News