Daily Archives: July 14, 2022

Sometimes an AI-based system cant decipher the physical world with a sufficient degree of accuracy and the option of just adding more data isnt possible. In many of these cases, however, this deficiency can be addressed by using four techniques to help AI better understand the physical world: synergize AI with scientific laws, augment data with expert human insights, employ devices to explain how AI makes decisions, and use other models to predict behavior.

Artificial intelligence (AI) gets its intelligence by analyzing a given dataset and detecting patterns. It has no concept of the world beyond this dataset, which creates a variety of dangers.

One changed pixel could confuse the AI system to think a horse is a frog or, even scarier, err on a medical diagnosis or a machine operation. Its exclusive reliance on the data sets also introduces a serious security vulnerability: Malicious agents can spoof the AI algorithm by introducing minor, nearly undetectable changes in the data. Finally, the AI system does not know what it does not know, and it can make incorrect predictions with a high degree of confidence.

Adding more data cannot always surmount these problems because practical business and technical constraints always limit the amount of data. And processing large datasets requires ever-larger AI models that are outpacing available hardware and growing AIs carbon footprint unsustainably.

We have identified an alternative remedy: connecting data-driven AI with other scientific or human inputs about the applications domain. It is based on our two decades of experience at the University of Californias Center for Information Technology Research in the Interest of Society and the Banatao Institute (CITRIS) in working with academics and business executives to implement AI for many applications. There are four ways it can be done.

We can combine available data with relevant laws of physics, chemistry, and biology to leverage the strengths and overcome the weaknesses of each. One example is an ongoing project with Komatsu where we are exploring how to use AI to guide the autonomous, efficient operation of heavy excavation equipment. AI does well in running the machine but not so well in understanding the surrounding environment.

Therefore, to teach the AI algorithm the differences between soft soil, gravel, and hard rock in the terrain being excavated we used physics-based models that describe size, distribution, hardness, and water content of the particles. Equipped with this knowledge, the AI-driven machine can apply just the right amount of force to grab a bucketful of earth efficiently and safely. Similarly, we use AI to operate a robotic surgical arm, and then combine it with a physics-based model that predicts how skin and tissue will deform under pressure. In both cases, whether earth or tissue, combining data-driven and physics-based models makes the operation safer, faster, and more efficient.

When available data is limited, human intuition can be used to augment and improve the intelligence of AI. For example, in the field of advanced manufacturing, it is extremely expensive and challenging to develop novel process recipes required to build a new product. Data about novel processes is limited or simply does not exist and generating it would require lots of trial-and-error attempts that may take many months and cost millions of dollars.

A more effective way is to have humans and AI augment and support each other, according to Lam Research, a leading maker of semiconductor equipment that supplies state-of-the-art microelectronics manufacturing facilities. Starting from scratch, highly experienced engineers usually do well in arriving at an approximately correct recipe, while AI is continuously collecting data and learning from those efforts. Once the recipe is in the ballpark, the engineers can enlist AI to support them in fine-tuning it to a precise optimum. Such techniques may provide up to an order of magnitude improvement in efficiency.

In the science fiction novel The Hitchhikers Guide to the Galaxy the smartest computer gave 42 as the answer to life, the universe, and everything, prompting many a reader to chuckle. Yet, it is no laughing matter for businesses, because AI often operates as a black box that makes confident recommendations without explaining why. If the way that AI makes decisions is not explainable, it is usually not actionable. A doctor shouldnt make a medical diagnosis and a utility engineer shouldnt shut off a critical piece of infrastructure based on an AI recommendation that they cannot explain intuitively.

For example, we are working on a smart infrastructure application where sensors monitor the integrity of thousands of wind turbines. The AI algorithm analyzing this data may throw a red flag when it detects a pattern of increased temperature or vibration intensity. But what does this mean? Is it just a hot day or a stray gust of high wind? Or does a utility crew need to be rushed out (an expensive operation) immediately?

Our solution: add fiber-optic sensors to measure the actual physical strain in the turbine material. Then, when utility engineers cross-check the AI red flag with the actual strain in the turbine blade, they can determine the true urgency of the problem and choose the safest corrective action.

Data-driven AI works well within the boundaries of the dataset it has processed, analyzing behavior between actual observations, or interpolation. However, to extrapolate that is, to predict behavior in operating modes outside the available data we have to incorporate knowledge of the domain in question. Indeed, this is often the approach taken by many applications that employ digital twins to mirror the operation of a complex system such as a jet engine. A digital twin is a dynamic model that mirrors the exact state of an actual system at all times and uses sensors to keep the model updated in real time.

We used this effectively in our project with Siemens Technology on digital twins for smart buildings. We employed data-driven AI to model and control the normal operation of the building, and to diagnose problems. Then, we judiciously mixed in physics-grounded equations such as basic thermodynamic equations tracking the heat flow to the air conditioning system and living spaces to predict the buildings behavior in a novel setting. Using this approach, we could predict the buildings behavior with different heating or cooling equipment or while operating under unusual weather conditions. This enabled us to try alternate operational modes without endangering critical infrastructure or its users. We found this approach also works well in other applications such as smart manufacturing, construction, and autonomous vehicles ranging from automobiles to spacecraft.

As humans, we understand the world around us by using our senses in tandem. Given a steaming cup, we determine instantly that it is tea from its color, smell, and taste. Connoisseurs may go a step further and identify it as a Darjeeling first-flush tea. AI algorithms are trained and are limited by a particular dataset and do not have access to all the senses like we do. An AI algorithm trained only on images of cups of coffee may see this same steaming cup of tea and conclude it is coffee. Worse, it may do so with a high degree of confidence!

Any available dataset will always be incomplete, and processing ever-larger datasets is often not practical or environmentally sustainable. Instead, adding other forms of understanding of the domain in question can help make data-driven AI safer and more efficient and enable it to address challenges that it otherwise could not.

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Adding More Data Isn't the Only Way to Improve AI - HBR.org Daily

A new computer algorithm developed by the University of TorontosParham Aarabican store and recall information strategically just like our brains.

The associate professor in the Edward S. Rogers Sr. department of electrical and computer engineering, in the Faculty of Applied Science & Engineering,has also created an experimental tool that leverages the new algorithm to help people with memory loss.

Most people think of AI as more robot than human, says Aarabi, whose framework is explored in a paper being presentedthis week at the IEEE Engineering in Medicine and Biology Society Conferencein Glasgow.I think that needs to change.

In the past, computers have relied on their users to tell them exactly what information to store. But with the rise of artificial intelligence (AI) techniques such as deep learning and neural nets, there has been a move toward fuzzier approaches.

Ten years ago, computing was all about absolutes, says Aarabi. CPUs processed and stored memory data in an exact way to make binary decisions. There was no ambiguity.

Nowwe want our computers to make approximate conclusions and guess percentages. We want an image processor to tell us, for example, that theres a 10 per cent chance a picture contains a car and a 40 per cent chance that it contains a pedestrian.

Aarabi has extended this same fuzzy approach to storing and retrieving information by copying several properties that help humans determine what to remember and, just as critically, what to forget.

Studies have shown that we tend to prioritize more recent events over less recent ones. We also emphasize memories that are more important to usand we compress long narratives to their essentials.

For example, today I remember that I saw my daughter off to school, I made a promise that Id pay someone backand I promised that Id read a research paper, says Aarabi. But I dont remember every single second of what I experienced.

The capacity to overlook certain information could supercharge existing models of machine learning.

Today, machine learning algorithms trawl through millions of database entries, looking for patterns that will help them correctly associate a given input with a given output. Only after countless iterations does the algorithm eventually become accurate enough to deal with new problems that it hasnt already seen.

If bio-inspired artificial memory enables these algorithms to give prominence to the most relevant data, they could potentially arrive at meaningful results much more quickly.

The approach could also support tools that process natural language to help people with memory loss keep track of key information.

Aarabi and his team have set up such a tool using a simple email-based interface. It reminds participants of important information based on algorithmic priority and a relevant index of keywords.

Ultimately, its geared to people with memory loss, Aarabi says. It helps them remember things in a way thats very human, very soft, without overwhelming them. Most task management aids are too complicated and not useful in these circumstances.

The demo is free and available for anyone to play with; simply send an email tomem@roya.vcfor instructions.

Ive been using it myself, says Aarabi. The goal is to put the demo in peoples hands whether theyre dealing with significant memory degradation or just everyday pressures and see what feedback we get. The next steps would be to build partnerships in health care to test in a more comprehensive way.

These days, AI applications are increasingly found in many human-centred fields, says ProfessorDeepa Kundur, chair of the department of electrical and computer engineering. Professor Aarabi, by researching ways to better integrate AI with these softer areas, is looking to ensure that the potential of AI is fully realized in our society.

Aarabi says that this algorithm is just the beginning.

Biologically inspired memory may very well take AI a step closer to human-level capabilities.

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Researcher uses 'fuzzy' AI algorithms to aid people with memory loss - University of Toronto

In a world rife with opposing views, lets draw attention to something we can all agree on: if I show you my pen, and then hide it behind my back, my pen still exists even though you cant see it anymore. We can all agree it still exists, and probably has the same shape and color it did before it went behind my back. This is just common sense.

These common-sense laws of the physical world are universally understood by humans. Even two-month-old infants share this understanding. But scientists are still puzzled by some aspects of how we achieve this fundamental understanding. And weve yet to build a computer that can rival the common-sense abilities of a typically developing infant.

New research by Luis Piloto and colleagues at Princeton University which Im reviewing for an article in Nature Human Behaviour takes a step towards filling this gap. The researchers created a deep-learning artificial intelligence (AI) system that acquired an understanding of some common-sense laws of the physical world.

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The findings will help build better computer models that simulate the human mind, by approaching a task with the same assumptions as an infant.

Typically, AI models start with a blank slate and are trained on data with many different examples, from which the model constructs knowledge. But research on infants suggests this is not what babies do. Instead of building knowledge from scratch, infants start with some principled expectations about objects.

For instance, they expect if they attend to an object that is then hidden behind another object, the first object will continue to exist. This is a core assumption that starts them off in the right direction. Their knowledge then becomes more refined with time and experience.

The exciting finding by Piloto and colleagues is that a deep-learning AI system modeled on what babies do outperforms a system that begins with a blank slate and tries to learn based on experience alone.

The researchers compared both approaches. In the blank-slate version, the AI model was given several visual animations of objects. In some examples, a cube would slide down a ramp. In others, a ball bounced into a wall.

The model detected patterns from the various animations and was then tested on its ability to predict outcomes with new visual animations of objects. This performance was compared to a model that had principled expectations built in before it experienced any visual animations.

These principles were based on the expectations infants have about how objects behave and interact. For example, infants expect two objects should not pass through one another.

If you show an infant a magic trick where you violate this expectation, they can detect the magic. They reveal this knowledge by looking significantly longer at events with unexpected, or magic outcomes, compared to events where the outcomes are expected.

Infants also expect an object should not be able to just blink in and out of existence. They can detect when this expectation is violated as well.

Piloto and colleagues found the deep-learning model that started with a blank slate did a good job, but the model based on object-centered coding inspired by infant cognition did significantly better.

The latter model could more accurately predict how an object would move, was more successful at applying the expectations to new animations, and learned from a smaller set of examples (for example, it managed this after the equivalent of 28 hours of video).

Its clear learning through time and experience is important, but it isnt the whole story. This research by Piloto and colleagues is contributing insight to the age-old question of what may be innate in humans, and what may be learned.

Beyond that, its defining new boundaries for what role perceptual data can play when it comes to artificial systems acquiring knowledge. And it also shows how studies on babies can contribute to building better AI systems that simulate the human mind.

Article by Susan Hespos, Psychology Department at Northwestern University Evanston, Illinois, USA and Professor of Infant Studies at MARCS Institute, Western Sydney University

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

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Researchers trained this AI to think like a baby heres what happened - TNW

Artificial Intelligence (AI) feels like a popular buzzword in healthcare over the last few years. In a piece for Digital Health, David Newey, deputy CIO at The Royal Marsden NHS Foundation Trust, explores bias in AI and what needs to be done.

On the night of my birthday celebrations in December 2021, I received an email followed by several texts alerting me to the cyber vulnerability that was log4j. This piece of innocuous code was originally developed in January 2001 as part of the apache logging services and was in use in technology stacks worldwide. Written 21 years ago without forethought to its future, this code was to prove a cyber security headache for at least 4 months as hackers looked for ways to exploit it.

This example demonstrated the fact that code has consequences, and in particular, historic code has consequences. Even now we can look to other systems such as IBM Z/OS mainframes which, still to this day run COBOL and FORTRAN dating back to the 1950s, but are now happily virtualised and still working for the US Department of Defence.

But as well its impact on legacy code, time also affects societal attitudes and changing demographics. For marginalised communities, the experience continues to change and evolve, sometimes for the better, sometimes for the worse; but nonetheless they change. Contemporary examples demonstrating a shift in attitudes include the redaction or warnings placed on literature such as Charlotte Brontes Jane Eyre or Charles Dickenss Great Expectations; or the tearing down of statues such as Edward Colston in Bristol. These illustrate how much society has changed since 1958 when the oldest piece of working software code, US Department of Defence MOSCAT (Mechanisation of Contract Administration Services) was written.

Consider now the development of AI machine learning, and its utility in the field of healthcare. The utility of AI is already hotly contested with proponents seeing it as a way to revolutionise medicine, such as in the rapid detection of abnormalities from CT images or digital pathology slides. AI is increasingly being seen as a way to sure up services that already have an acute shortage of trained staff, fighting against a backlog Tsunami driven from Covid-19.

Opponents say that AI is another over-hyped technology which, along with virtual reality, blockchain, NFTs and cryptocurrency, is destined to end up on the heap of technological white elephants. Opponents would point to IBMs recent move to sell Watson as an example of blue chip companies deciding that it just isnt worth it. Yet the reality is that AI is here to stay, and just like the advent of the desktop PC, the internet and the mobile device is rapidly moving up the adoption curve, growing more powerful in line with Moores law.

The introduction of bias

Simply put, AI relies on two key components; the development of an algorithm, and the use training data to develop a propensity model to predict outcomes. It is here that various factors can introduce bias and hard bake into an algorithm societal injustice.

Bias in development of AI can be introduced through a number of ways:

For the purposes of this article however, it is temporal bias that we seek to consider.

Just like societal values, an AI algorithm is affected by temporal factors derived from:

As a result, and despite best efforts to eliminate bias, code written in 2022 could legitimately be out of date within 5 years or less depending on the changes that occur in those factors over time. For example, if an AI algorithm was developed in 1981 to provide clinician decision support for HIV patients; how applicable would its advice be in 2022? Would it be aware of the use of antiretroviral therapies or the change in communities affected?

So how can this be addressed?

There is precedent for addressing these types of concern. The Medicines and Healthcare products Regulatory Agency (MHRA) for example provides ongoing pharmacovigilance for newly licenced drugs involving:

Feedback about drugs is captured through an adverse reaction reporting system (Yellow Card Scheme) as well as ongoing research studies, published literature and morbidity and mortality databases.

Government intervention

Already General Data Protection Regulation (GDPR) legislation has provisions regarding the need to for organisations to provide transparency around the use of AI based decisions; and includes the ability for an individual to opt out of being subject to an AI based decision that has legal or similarly significant effects.

In December 2021, the government published a roadmap to develop a regulatory framework to address the use of AI in real-world applications including the need for the MHRA to expand its remit to incorporate AI. Prior to its merger with NHS England and Improvement, NHSX had begun to consider this issue; but until legislation is passed there are still plenty of examples of historical and contemporary AI algorithms that are out in the wild.

Local changes for best practice

Much like best practice derived from Information Technology Infrastructure Library (ITIL) for the creation of application and data registers. It is should now be incumbent on CIOs to now urgently look at their own digital ecosystem and put governance measures in place to both track and review regularly the AI algorithms in use within their organisations.

An AI oversight committee should be formed that regularly approves the use AI algorithms against set criteria addressing applicability and bias, as well as setting licenced use dates for software after which it should either be re-licenced or taken out of service. By being proactive at this stage, organisations can get ahead of the curve and be in the best possible place to comply with future legislation and regulation.

Time to change

In summary, we are now at a pivotal point whereby AI algorithms developed now could affect the way in which future generations are treated, potentially hard-baking in inequality and societal injustice. It is a given that use of artificial intelligence in healthcare will only increase, however IT professionals need to be conscious of the effects of the passage of time on the development and use of AI algorithms in every area that affects individuals and communities. Despite the fact that it is only a matter of time before government regulation takes effect, CIOs should now put steps in place at organisational level to both prevent and mitigate the negative effects of bias which may be introduced into such algorithms either at inception or arising from the passage of time.

After all time waits for no one.

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Bias in AI: It's a matter of time - Digital Health

TOKYO--(BUSINESS WIRE)--Elix, Inc., an AI drug discovery company with the mission of Rethinking Drug Discovery (CEO: Shinya Yuki/Headquarters: Tokyo; hereafter referred to as "Elix") launched Elix Discovery, the only all-in-one platform that provides everything needed for AI drug discovery, from models for property prediction and molecular design to AI consulting and implementation support.

Drug discovery using artificial intelligence (AI) and machine learning is attracting attention to shorten the time required for drug discovery, reduce associated costs, and improve success rates. However, pharmaceutical companies face major barriers in the adoption of AI due to insufficient knowledge of AI technology, difficulty in understanding how to use existing AI tools, lack of an intuitive and integrated platform, and the need to consider the best way to utilize AI for each specific drug discovery project.

Elix Discovery is the only all-in-one platform that provides a wide selection of AI models, intuitive Graphical User Interface (GUI), expert implementation support and consulting, and knowledge for AI drug discovery. The platform provides essential features for AI drug discovery, and will continue to grow from ongoing updates as new developments are made in the ever-evolving AI field. While many AI companies are secretive and do not disclose their knowledge and methods to clients, Elix, Inc. values transparency and openly shares knowledge with our clients to help them accumulate experience in AI drug discovery.

Key modules of Elix Discovery

Features of Elix Discovery

Pharmaceutical companies need multiple types of AI models and data processing functions to conduct AI drug discovery, and Elix Discovery is an all-in-one platform that implements these models and functions. From property prediction to molecular design, Elix Discovery has all the essential functions for AI drug discovery, and provides them in a modular architecture that allows users to leverage these functions seamlessly across the platform.

Many pharmaceutical companies feel the need to have their own AI drug discovery platform. However, building the entire system in-house entails a number of difficulties, including the many years and large costs required for development, hiring and training of talented engineers with expertise in multiple fields, and the need for on-going updates and technical support. By introducing Elix Discovery, which has already been developed specifically for AI drug discovery and tested in the field by existing users, a high-quality environment for AI drug discovery can be established in a short period of time. Furthermore, the platform is continuously updated, allowing for the easy and rapid adoption of cutting-edge technology in a fast changing field.

Pharmaceutical companies have been slow to utilize AI not only because of the lack of superior AI tools, but also a lack of know-how about AI in specific, individual situations that occur in drug discovery. In cases where pharmaceutical companies rely on consulting AI companies, it may not be possible to accumulate in-house expertise.

Elixs researchers, who have consulting experience with numerous companies, will provide support while openly sharing their knowledge, from proposing solutions to specific issues to lectures on how to use the platform and background knowledge about AI and deep learning. For complex issues, Elixs engineers will work on behalf of the client to create and analyze AI models, and provide them in a format that can be reproduced by the client.

Even in pharmaceutical companies that employ computational chemists, the small number of such chemists limits the support that they can provide to medicinal chemists in the use of AI. Elix provides a complete, integrated platform and consulting service for AI drug discovery, including the training of medicinal chemists who can utilize AI via the Elix Discovery platform.

Drug discovery is by no means a challenge that can be solved by AI alone; the knowledge of the medicinal chemist is critically important. Elix Discovery is a platform developed with this philosophy in mind, and designed to let the medicinal chemist leverage the power of AI and deep learning. Even medicinal chemists with limited knowledge of AI can use the system intuitively without worrying about choosing AI parameters and other details thanks to an automated optimization procedure when creating AI models. In addition, computational chemists who are more familiar with AI can perform detailed configurations, and easily share the models they create on the platform with medicinal chemists for immediate use.

Kaken Pharmaceutical Co., Ltd. has concluded a contract to use the Elix Discovery

Elix has already provided the Elix Discovery platform to Kaken Pharmaceutical Co., Ltd. (President and Representative Director: Hiroyuki Horiuchi/Headquarters: Tokyo; hereinafter referred to as Kaken), and began consulting and supporting operations for AI drug discovery at its Drug Discovery Center (in Kyoto) in May 2022.

Kaken was established in 1948 with its roots in the Riken Foundation, which has produced many extremely important scientists in the history of Japanese science, including Yoshio Nishina, considered the father of modern physics, as well as Nobel Prize winners Hideki Yukawa and Shinichiro Tomonaga. Starting with manufacturing and sale of penicillin in the same year, Kaken has introduced many ethical drugs, including Mentax, Fibrast, and, more recently, Clenafin and Regroth, with particular strength in therapeutic areas such as dermatology and orthopedics (https://www.kaken.co.jp/english/).

Elix will continue to contribute to solving challenges in drug discovery through the on-going development of Elix Discovery.

About Elix, Inc.

Elix, Inc. is an AI drug discovery company with the mission of Rethinking drug discovery. We are developing our business for pharmaceutical companies, universities, and research institutions by focusing on machine learning to reduce the enormous cost and time involved in drug discovery and improve its success rate. Visit https://www.elix-inc.com/ for more details.

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Elix Launches Elix Discovery, the Only All-in-one Platform That Provides Everything Needed for AI Drug Discovery, from Models for Property Prediction...