Monthly Archives: July 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.

More here:

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