Picture a tray. On the tray is an assortment of shapes: Some cubes, others spheres. The shapes are made from a variety of different materials and represent an assortment of sizes. In total there are, perhaps, eight objects. My question: Looking at the objects, are there an equal number of large things and metal spheres?
Its not a trick question. The fact that it sounds as if it is is proof positive of just how simple it actually is. Its the kind of question that a preschooler could most likely answer with ease. But its next to impossible for todays state-of-the-art neural networks. This needs to change. And it needs to happen by reinventing artificial intelligence as we know it.
Thats not my opinion; its the opinion of David Cox, director of the MIT-IBM Watson A.I. Lab in Cambridge, MA. In a previous life, Cox was a professor at Harvard University, where his team used insights from neuroscience to help build better brain-inspired machine learning computer systems. In his current role at IBM, he oversees work on the companys Watson A.I. platform.Watson, for those who dont know, was the A.I. which famously defeated two of the top game show players in history at TV quiz show Jeopardy. Watson also happens to be a primarily machine-learning system, trained using masses of data as opposed to human-derived rules.
So when Cox says that the world needs to rethink A.I. as it heads into a new decade, it sounds kind of strange. After all, the 2010s has been arguably the most successful ten-year in A.I. history: A period in which breakthroughs happen seemingly weekly, and with no frosty hint of an A.I. winter in sight.This is exactly why he thinks A.I. needs to change, however. And his suggestion for that change, a currently obscure term called neuro-symbolic A.I., could well become one of those phrases were intimately acquainted with by the time the 2020s come to an end.
Neuro-symbolic A.I. is not, strictly speaking, a totally new way of doing A.I. Its a combination of two existing approaches to building thinking machines; ones which were once pitted against each as mortal enemies.
The symbolic part of the name refers to the first mainstream approach to creating artificial intelligence. From the 1950s through the 1980s, symbolic A.I. ruled supreme. To a symbolic A.I. researcher, intelligence is based on humans ability to understand the world around them by forming internal symbolic representations. They then create rules for dealing with these concepts, and these rules can be formalized in a way that captures everyday knowledge.
If the brain is analogous to a computer, this means that every situation we encounter relies on us running an internal computer program which explains, step by step, how to carry out an operation, based entirely on logic. Provided that this is the case, symbolic A.I. researchers believe that those same rules about the organization of the world could be discovered and then codified, in the form of an algorithm, for a computer to carry out.
Symbolic A.I. resulted in some pretty impressive demonstrations. For example, in 1964 the computer scientist Bertram Raphael developed a system called SIR, standing for Semantic Information Retrieval. SIR was a computational reasoning system that was seemingly able to learn relationships between objects in a way that resembled real intelligence. If you were to tell it that, for instance, John is a boy; a boy is a person; a person has two hands; a hand has five fingers, then SIR would answer the question How many fingers does John have? with the correct number 10.
there are concerning cracks in the wall that are starting to show.
Computer systems based on symbolic A.I. hit the height of their powers (and their decline) in the 1980s. This was the decade of the so-called expert system which attempted to use rule-based systems to solve real-world problems, such as helping organic chemists identify unknown organic molecules or assisting doctors in recommending the right dose of antibiotics for infections.
The underlying concept of these expert systems was solid. But they had problems. The systems were expensive, required constant updating, and, worst of all, could actually become less accurate the more rules were incorporated.
The neuro part of neuro-symbolic A.I. refers to deep learning neural networks. Neural nets are the brain-inspired type of computation which has driven many of the A.I. breakthroughs seen over the past decade. A.I. that can drive cars? Neural nets. A.I. which can translate text into dozens of different languages? Neural nets. A.I. which helps the smart speaker in your home to understand your voice? Neural nets are the technology to thank.
Neural networks work differently to symbolic A.I. because theyre data-driven, rather than rule-based. To explain something to a symbolic A.I. system means explicitly providing it with every bit of information it needs to be able to make a correct identification. As an analogy, imagine sending someone to pick up your mom from the bus station, but having to describe her by providing a set of rules that would let your friend pick her out from the crowd. To train a neural network to do it, you simply show it thousands of pictures of the object in question. Once it gets smart enough, not only will it be able to recognize that object; it can make up its own similar objects that have never actually existed in the real world.
For sure, deep learning has enabled amazing advances, David Cox told Digital Trends. At the same time, there are concerning cracks in the wall that are starting to show.
One of these so-called cracks relies on exactly the thing that has made todays neural networks so powerful: data. Just like a human, a neural network learns based on examples. But while a human might only need to see one or two training examples of an object to remember it correctly, an A.I. will require many, many more. Accuracy depends on having large amounts of annotated data with which it can learn each new task.
That makes them less good at statistically rare black swan problems. A black swan event, popularized by Nassim Nicholas Taleb, is a corner case that is statistically rare. Many of our deep learning solutions today as amazing as they are are kind of 80-20 solutions, Cox continued. Theyll get 80% of cases right, but if those corner cases matter, theyll tend to fall down. If you see an object that doesnt normally belong [in a certain place], or an object at an orientation thats slightly weird, even amazing systems will fall down.
Before he joined IBM, Cox co-founded a company, Perceptive Automata, that developed software for self-driving cars. The team had a Slack channel in which they posted funny images they had stumbled across during the course of data collection. One of them, taken at an intersection, showed a traffic light on fire. Its one of those cases that you might never see in your lifetime, Cox said. I dont know if Waymo and Tesla have images of traffic lights on fire in the datasets they use to train their neural networks, but Im willing to bet if they have any, theyll only have a very few.
Its one thing for a corner case to be something thats insignificant because it rarely happens and doesnt matter all that much when it does. Getting a bad restaurant recommendation might not be ideal, but its probably not going to be enough to even ruin your day. So long as the previous 99 recommendations the system made are good, theres no real cause for frustration. A self-driving car failing to respond properly at an intersection because of a burning traffic light or a horse-drawn carriage could do a lot more than ruin your day. It might be unlikely to happen, but if it does we want to know that the system is designed to be able to cope with it.
If you have the ability to reason and extrapolate beyond what weve seen before, we can deal with these scenarios, Cox explained. We know that humans can do that. If I see a traffic light on fire, I can bring a lot of knowledge to bear. I know, for example, that the light is not going to tell me whether I should stop or go. I know I need to be careful because [drivers around me will be confused.] I know that drivers coming the other way may be behaving differently because their light might be working. I can reason a plan of action that will take me where I need to go. In those kinds of safety-critical, mission-critical settings, thats somewhere I dont think that deep learning is serving us perfectly well yet. Thats why we need additional solutions.
The idea of neuro-symbolic A.I. is to bring together these approaches to combine both learning and logic. Neural networks will help make symbolic A.I. systems smarter by breaking the world into symbols, rather than relying on human programmers to do it for them. Meanwhile, symbolic A.I. algorithms will help incorporate common sense reasoning and domain knowledge into deep learning. The results could lead to significant advances in A.I. systems tackling complex tasks, relating to everything from self-driving cars to natural language processing. And all while requiring much less data for training.
Neural networks and symbolic ideas are really wonderfully complementary to each other, Cox said. Because neural networks give you the answers for getting from the messiness of the real world to a symbolic representation of the world, finding all the correlations within images. Once youve got that symbolic representation, you can do some pretty magical things in terms of reasoning.
For instance, in the shape example I started this article with, a neuro-symbolic system would use a neural networks pattern recognition capabilities to identify objects. Then it would rely on symbolic A.I. to apply logic and semantic reasoning to uncover new relationships. Such systems have already been proven to work effectively.
Its not just corner cases where this would be useful, either. Increasingly, it is important that A.I. systems are explainable when required. A neural network can carry out certain tasks exceptionally well, but much of its inner reasoning is black boxed, rendered inscrutable to those who want to know how it made its decision. Again, this doesnt matter so much if its a bot that recommends the wrong track on Spotify. But if youve been denied a bank loan, rejected from a job application, or someone has been injured in an incident involving an autonomous car, youd better be able to explain why certain recommendations have been made. Thats where neuro-symbolic A.I. could come in.
A few decades ago, the worlds of symbolic A.I. and neural networks were at odds with one another. The renowned figures who championed the approaches not only believed that their approach was right; they believed that this meant the other approach was wrong. They werent necessarily incorrect to do so. Competing to solve the same problems, and with limited funding to go around, both schools of A.I. appeared fundamentally opposed to each other. Today, it seems like the opposite could turn out to be true.
Its really fascinating to see the younger generation, Cox said. Most of my team are relatively junior people: fresh, excited, fairly recently out of their Ph.Ds. They just dont have any of that history. They just dont care [about the two approaches being pitted against each other] and not caring is really powerful because it opens you up and gets rid of those prejudices. Theyre happy to explore intersections They just want to do something cool with A.I.
Should all go according to plan, all of us will benefit from the results.
Read more from the original source:
- artificial intelligence | Definition, Examples, and ... - March 28th, 2020
- What is Artificial Intelligence? How Does AI Work? | Built In - March 28th, 2020
- Benefits & Risks of Artificial Intelligence - Future of ... - March 28th, 2020
- Return On Artificial Intelligence: The Challenge And The Opportunity - Forbes - March 28th, 2020
- VA Looking to Expand Usage of Artificial Intelligence Data - GovernmentCIO Media - March 28th, 2020
- Global Artificial Intelligence in Healthcare Market - Premium Insight, Competitive News Feed Analysis, Company Usability Profiles, Market Sizing &... - March 28th, 2020
- The Global Artificial Intelligence in Aviation Market is expected to grow from USD 214.36 Million in 2018 to USD 1,824.46 Million by the end of 2025... - March 28th, 2020
- Is artificial intelligence the answer to disease prevention? - The Burn-In - March 28th, 2020
- Why transparency is key to promoting trust in artificial intelligence - IT PRO - March 28th, 2020
- Coronavirus: Spain to use artificial intelligence to automate testing - ComputerWeekly.com - March 28th, 2020
- Bridging the gaps: joining human and artificial intelligence | Technology - Business Chief Canada - March 28th, 2020
- DIAGNOS Will Utilize its Artificial Intelligence Medical Platform FLAIRE in Response to the US White House - Call to Action to Analyse and Transform... - March 26th, 2020
- LiveMD Global Telehealth Platform launches artificial intelligence tracking and triaging tools to help combat COVID-19 (CoronaVirus) Pandemic -... - March 26th, 2020
- New Research from Newark Reveals Strong Adoption of Artificial Intelligence within the Internet of Things Ecosystem - Embedded Computing Design - March 26th, 2020
- AI vs COVID-19: Here are the AI tools and services fighting coronavirus - AI News - March 26th, 2020
- Stanford virtual conference to focus on COVID19 and artificial intelligence | Stanford News - Stanford University News - March 26th, 2020
- Artificial Intelligence in the energy sector: opportunities and challenges - WhaTech - March 26th, 2020
- 31 Companies, Products and People making Artificial Intelligence a Reality in 2020 - PR Web - March 26th, 2020
- KT zu Guttenberg, Artificial Intelligence and You - theTrumpet.com - March 26th, 2020
- BrainChip and Socionext Provide a New Low-Power Artificial Intelligence Platform for AI Edge Applications - Design and Reuse - March 26th, 2020
- Artificial Intelligence Chipsets Market report reviews overview with demographic data and industry growth trends by 2025 - WhaTech Technology and... - March 26th, 2020
- Richmond-based Blue Heron Capital invests in company that uses artificial intelligence to improve lung disease monitoring - Richmond.com - March 26th, 2020
- Artificial Intelligence is Becoming the Future of Investment Platforms - EnterpriseTalk - March 19th, 2020
- Is Your Company Using Artificial Intelligence To Transform An Industry? Nominations For The Forbes 2020 AI 50 List Are Now Open - Forbes - March 19th, 2020
- On the Role of Artificial Intelligence in Genomics to Enhance Precisio | PGPM - Dove Medical Press - March 19th, 2020
- Artificial intelligence myths: Reality check - Livemint - March 19th, 2020
- Battery Researchers Look to Artificial Intelligence to Slash Recharging Times - Greentech Media News - March 19th, 2020
- The Army Will Soon Be Able to Command Robot Tanks With Artificial Intelligence - The National Interest - March 19th, 2020
- Canon Medical's 3T MR System Receives FDA Clearance for Artificial Intelligence-Based Image Reconstruction Technology - BioSpace - March 19th, 2020
- Coronavirus: How Artificial Intelligence, Data Science And Technology Is Used To Fight The Pandemic - Forbes - March 19th, 2020
- Artificial intelligence: The new power dynamic of today - Daily Sabah - March 19th, 2020
- Rethinking Financial Services with Artificial Intelligence Tools - The Financial Brand - March 19th, 2020
- Artificial intelligence recruited to find clues about Covid-19 - The Star Online - March 19th, 2020
- IIT-M to reskill women in artificial intelligence - Campus Varta - March 19th, 2020
- San Diego-Based Company takes Digital Marketing to the next Level by Launching the First Artificial Intelligence Marketing Agency in the United States... - March 19th, 2020
- An Unexpected Ally in the War With Bacteria - The Atlantic - March 19th, 2020
- Compliance For A Digital World: BSA/AML The New ABC's: Artificial Intelligence, Blockchain And How Each Complements The Other - JD Supra - March 19th, 2020
- The next step in digital transformation: is Artificial Intelligence production-ready for green sand foundries? - Foundry-Planet.com - March 19th, 2020
- Insights into the North America Artificial Intelligence in Fashion Market to 2027 - Drivers, Restraints, Opportunities and Trends -... - March 19th, 2020
- Artificial Intelligence by CWI and Amsterdam UMC proposes the best radiation treatment plans in clinical practice for the first time - Centrum... - March 19th, 2020
- H2O.ai Named to the 2020 CB Insights AI 100 List of Most Innovative Artificial Intelligence Startups - Yahoo Finance - March 4th, 2020
- 'There's No Story That Stays Stable for Too Long.' How Artists Are Using Artificial Intelligence to Confront Modern Anxieties - TIME - March 4th, 2020
- Minister declares creation of artificial intelligence centres in Poland - The First News - March 4th, 2020
- Dont forget to consider GDPR when using artificial intelligence in the workplace - ComputerWeekly.com - March 4th, 2020
- Iktos and SRI International Announce Collaboration to Combine Artificial Intelligence and Novel Automated Discovery Platform for Accelerated... - March 4th, 2020
- Why we need to adapt existing EU laws to Artificial Intelligence - European Public Health Alliance - March 4th, 2020
- How a Portland nonprofit is using artificial intelligence to help save whales, giraffes, zebras - Seattle Times - March 4th, 2020
- Artificial Intelligence Infused with Big Data Creating a Tech-driven World - EnterpriseTalk - March 4th, 2020
- Gaming with Artificial Intelligence Technology in 2020 - ReadWrite - March 4th, 2020
- Global Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024 | Growing Adoption of Cloud Based Solutions to Boost Market Growth | Technavio -... - March 4th, 2020
- WorldMarkets Continues With the Success of Its Trading Artificial Intelligence - Live Bitcoin News - March 4th, 2020
- Why Artificial Intelligence Will Never Beat the Stock Market - Traders Magazine - March 4th, 2020
- Welcome to the roaring 2020s, the artificial intelligence decade - GreenBiz - January 5th, 2020
- Top five projections in Artificial Intelligence for 2020 - Economic Times - January 5th, 2020
- A reality check on artificial intelligence: Can it match the hype? - PhillyVoice.com - January 5th, 2020
- Can medical artificial intelligence live up to the hype? - Los Angeles Times - January 5th, 2020
- Illinois regulates artificial intelligence like HireVues used to analyze online job Interviews - Vox.com - January 5th, 2020
- How This Cofounder Created An Artificial Intelligence Styling Company To Help Consumers Shop - Forbes - January 5th, 2020
- The U.S. Patent and Trademark Office Takes on Artificial Intelligence - JD Supra - January 5th, 2020
- Baidu looks to work with Indian institutions on AI - BusinessLine - January 5th, 2020
- Top Movies Of 2019 That Depicted Artificial Intelligence (AI) - Analytics India Magazine - January 5th, 2020
- Shocking ways AI technology will revolutionise every day industries in YOUR lifetime - Express.co.uk - January 5th, 2020
- Artificial intelligence takes scam to a whole new level - The Jackson Sun - January 5th, 2020
- Global Industrial Artificial Intelligence Market 2019 Research by Business Analysis, Growth Strategy and Industry Development to 2024 - Food &... - January 5th, 2020
- IIT Hyderabad to collaborate with Telangana government on artificial intelligence - India Today - January 5th, 2020
- THE AI IN TRANSPORTATION REPORT: How automakers can use artificial intelligence to cut costs, open new revenue - Business Insider India - January 5th, 2020
- Revisiting the rise of A.I.: How far has artificial intelligence come since 2010? - Digital Trends - December 30th, 2019
- Artificial intelligence is helping us talk to animals (yes, really) - Wired.co.uk - December 30th, 2019
- Artificial Intelligence Identifies Previously Unknown Features Associated with Cancer Recurrence - Imaging Technology News - December 30th, 2019
- AI IN BANKING: Artificial intelligence could be a near $450 billion opportunity for banks - here are the strat - Business Insider India - December 30th, 2019
- Artificial Intelligence Is Rushing Into Patient Care - And Could Raise Risks - Scientific American - December 30th, 2019
- Quantum leap: Why we first need to focus on the ethical challenges of artificial intelligence - Economic Times - December 30th, 2019
- In 2020, lets stop AI ethics-washing and actually do something - MIT Technology Review - December 30th, 2019
- The Power Of Purpose: How We Counter Hate Used Artificial Intelligence To Battle Hate Speech Online - Forbes - December 30th, 2019
- The skills needed to land the hottest tech job of 2020 - Business Insider Nordic - December 30th, 2019
- In the 2020s, human-level A.I. will arrive, and finally ace the Turing test - Inverse - December 30th, 2019
- Samsung to announce its Neon artificial intelligence project at CES 2020 - Firstpost - December 30th, 2019
- How Artificial Intelligence Is Totally Changing Everything - HowStuffWorks - December 26th, 2019
- Artificial intelligence jobs on the rise, along with everything else AI - ZDNet - December 26th, 2019
- Why Cognitive Technology May Be A Better Term Than Artificial Intelligence - Forbes - December 26th, 2019