This AI Researcher Thinks We Have It All Wrong – Forbes
Posted: February 23, 2020 at 12:50 pm
Dr. Luis Perez-Breva
Luis Perez-Breva is an MIT professor and the faculty director of innovation teams at the MIT School or Engineering. He is also an entrepreneur and part of The Martin Trust Center for MIT Entrepreneurship. Luis works to see how we can use technology to make our lives better and also on how we can work to get new technology out into the world. On a recent AI Today podcast, Professor Perez-Breva managed to get us to think deeply into our understanding of both artificial intelligence and machine learning.
Are we too focused on data?
Anyone who has been following artificial intelligence and machine learning knows the vital centrality of data. Without data, we cant train machine learning models. And without machine learning models, we dont have a way for systems to learn from experience. Surely, data needs to be the center of our attention to make AI systems a reality.
However, Dr. Perez-Breva thinks that we are overly focusing on data and perhaps that extensive focus is causing goals for machine learning and AI to go astray. According to Luis, so much focus is put into obtaining data that we judge how good a machine learning system is by how much data was collected, how large the neural network is, and how much training data was used. When you collect a lot of data you are using that data to build systems that are primarily driven by statistics. Luis says that we latch onto statistics when we feed AI so much data, and that we ascribe to systems intelligence, when in reality, all we have done is created large probabilistic systems that by virtue of large data sets exhibit things we ascribe to intelligence. He says that when our systems arent learning as we want, the primary gut reaction is to give these AI system more data so that we dont have to think as much about the hard parts about generalization and intelligence.
Many would argue that there are some areas where you do need data to help teach AI. Computers are better able to learn image recognition and similar tasks by having more data. The more data, the better the networks, and the more accurate the results. On the podcast, Luis asked whether deep learning is great enough that this works or if we have a big enough data set that image recognition now works. Basically: is it the algorithm or just the sheer quantity of data that is making this work?
Rather, what Luis argues is that if we can find a better way to structure the system as a whole, then the AI system should be able to reason through problems, even with very limited data. Luis compares using machine learning in every application to the retail world. He talks about how physical stores are seeing the success in online stores and trying to copy on that success. One of the ways they are doing this is by using apps to navigate stores. Luis mentioned that he visited a Target where he had to use his phone to navigate the store which was harder than being able to look at signs. Having a human to ask questions and talk to is both faster and part of the experience of being in a brick and mortar retail location. Luis says he would much rather have a human to interact with at one of these locations than a computer.
Is the problem deep learning?
He compares this to machine learning by saying that machine learning has a very narrow application. If you try to apply machine learning to every aspect of AI that you will end up with issues like he did at the Target. Basically looking at neural networks as a hammer and every AI problem as a nail. No one technology or solution works for every application. Perhaps deep learning only works because of vast quantities of data? Maybe theres a better algorithm that can generalize better, apply knowledge learned in one domain to another better, and use smaller amounts of data to get much better quality insights.
People have tried recently to automate many of the jobs that people do. Throughout history, Luis says that technology has killed businesses when it tries to replace humans. Technology and businesses are successful when they expand on what humans can do. Attempting to replace humans is a difficult task and one that is going to lead companies down the road to failure. As humans, he points out, we crave human interaction. Even the age that is constantly on their technology desires human interaction greatly.
Luis also makes a point that while many people mistakenly confuse automation and AI. Automation is using a computer to carry out specific tasks, it is not the creation of intelligence. This is something that many are mentioning on several occasions. Indeed, its the fear of automation and the fictional superintelligence that has many people worried about AI. Dr. Perez-Breva makes the point that many ascribe to machines human characteristics. But this should not be the case with AI system.
Rather, he sees AI systems more akin to a new species with a different mode of intelligence than humans. His opinion is that researchers are very far from creating an AI that is similar to what you will find in books and movies. He blames movies for giving people the impression of robots (AI) killing people and being dangerous technologies. While there are good robots in movies there are few of them and they get pushed to the side by bad robots. He points out that we need to move away from this pushing images of bad robots. Our focus needs to be on how artificial intelligence can help humans grow. It would be beneficial if the movie-making industry could help with this. As such, AI should be thought of as a new intelligent species were trying to create, not something that is meant to replace us.
A positive AI future
Despite negative images and talk, Luis is sure that artificial intelligence is here to stay. At least for a while. So many companies have made large investments into AI that it would be difficult for them to just stop using them or to stop the development.
As a final question in the interview, Luis was asked where he sees the industry of artificial intelligence going. Prefacing his answer with the fact that based on the earlier discussion people are investing in machine learning and not true artificial intelligence, Luis said that he is happy in the investment that businesses are making in what they call AI. He believes that these investments will help the development of this technology to stay around for a minimum of four years.
Once we can stop comparing humans to artificial intelligence, Luis believes that we will see great advancements in what AI can do. He believes that AI has the power to work alongside humans to unlock knowledge and tasks that we werent previously able to do. The point when this happens, he doesnt believe is that far away. We are getting closer to it every day.
Many of Luiss ideas are contrary to popular beliefs by many people who are interested in the world of artificial intelligence. At the same time, these ideas that he presents are presented in a very logical manner and are very thought-provoking. The only way that we will be able to see what is right or where his ideas go is time.
Link:
This AI Researcher Thinks We Have It All Wrong - Forbes
- Getting Started With Machine Learning: Definition and Applications - CMSWire - February 20th, 2021
- This Biotech Company Combines Single Cell Genomics with Machine Learning (ML) Algorithms To Enable High Resolution Profiling of the Immune System -... - February 20th, 2021
- Immunai Raises $60M to Decode the Immune System with Machine Learning and AI - AlleyWatch - February 20th, 2021
- Cloud Machine Learning Market: Indoor Applications Projected to be the Most Attractive Segment during 2021-2029 KSU | The Sentinel Newspaper - KSU |... - February 20th, 2021
- Machine Learning in Insurance Market: Indoor Applications Projected to be the Most Attractive Segment during 2021-2029 KSU | The Sentinel Newspaper -... - February 20th, 2021
- Carin Meier Using Machine Learning to Combat Major Illness, such as the Coronavirus - InfoQ.com - February 20th, 2021
- Moffitt Cancer Center: Why we are building the first machine learning department in oncology - The Cancer Letter - February 20th, 2021
- Machine Learning and where is it used? - Tech Guide - February 20th, 2021
- Artificial Intelligence and Machine Learning for Insurance Technology from Johnson Controls Available on the Ocean Tomo Bid-Ask Market - Yahoo Finance - February 20th, 2021
- Identifying COVID-19 Therapy Candidates With Machine Learning - Contagionlive.com - February 20th, 2021
- Machine Learning in Tax and Accounting Market gigantic revenues by 2028 with Amazon Web Services, Baidu Inc, Google, Intel, IBM, Hewlett Packard,... - February 20th, 2021
- Using AI and Machine Learning will increase in horti industry - hortidaily.com - February 13th, 2021
- The head of JPMorgan's machine learning platform explained what it's like to work there - eFinancialCareers - February 13th, 2021
- If you know nothing about deep learning with Python, start here - TechTalks - February 13th, 2021
- Mental health diagnoses and the role of machine learning - Health Europa - February 13th, 2021
- 5 Ways the IoT and Machine Learning Improve Operations - BOSS Magazine - February 13th, 2021
- There Is No Silver Bullet Machine Learning Solution - Analytics India Magazine - February 13th, 2021
- Postdoctoral Research Associate in Digital Humanities and Machine Learning job with DURHAM UNIVERSITY | 246392 - Times Higher Education (THE) - February 13th, 2021
- The Collision of AI's Machine Learning and Manipulation: Deepfake Litigation Risks to Companies from a Product Liability, Privacy, and Cyber... - February 13th, 2021
- Parascript and SFORCE Partner to Leverage Machine Learning Eliminating Barriers to Automation - GlobeNewswire - February 13th, 2021
- Rackspace Technology Study uncovers AI and Machine Learning knowledge gap in the UAE - Intelligent CIO ME - February 13th, 2021
- How Blockchain and Machine Learning Impact on education system - ABCmoney.co.uk - February 13th, 2021
- Mission Healthcare of San Diego Adopts Muse Healthcare's Machine Learning Tool - Southernminn.com - January 19th, 2021
- Deep Learning Outperforms Standard Machine Learning in Biomedical Research Applications, Research Shows - Georgia State University News - January 19th, 2021
- Project MEDAL to apply machine learning to aero innovation - The Engineer - January 19th, 2021
- Forecast On Machine Learning (ML) Intelligent Process Automation Market Witness the Growth of Great Billion by 2027 With Top Companies Like Automation... - January 19th, 2021
- Machine Learning Shown to Identify Patient Response to Sarilumab in Rheumatoid Arthritis - AJMC.com Managed Markets Network - January 19th, 2021
- Bangalore based Great Learning can help you unleash the potential of an M-Tech in Data Science & Machine - Times of India - January 19th, 2021
- CERC plans to embrace AI, machine learning to improve functioning - Business Standard - January 19th, 2021
- NTT Co-authored Papers at NeurIPS to Advance Machine Learning Efficiency and Performance - Business Wire - December 7th, 2020
- Why Intel believes confidential computing will boost AI and machine learning - VentureBeat - December 3rd, 2020
- Machine Learning Market to Grow Notably Attributed to Increasing Adoption of Analytics-driven Solutions by Developing Economies, says Fortune Business... - December 3rd, 2020
- Machine learning: The new language of data and analytics - ITProPortal - December 3rd, 2020
- Injecting Machine Learning And Bayesian Optimization Into HPC - The Next Platform - December 3rd, 2020
- QA Increasingly Benefits from AI and Machine Learning - RTInsights - December 3rd, 2020
- Everything to Know About Machine Learning as a Service (MLaaS) - Analytics Insight - December 3rd, 2020
- How the Food and Beverage Industry is Affected by Machine Learning and AI - IoT For All - December 3rd, 2020
- Amazon announces new machine learning tools to help customers monitor machines and worker safety - www.computing.co.uk - December 3rd, 2020
- Machine Learning and Location Data Applications Market 2020 Top Companies report covers, Industry Outlook, Top Countries Analysis & Top... - December 3rd, 2020
- Commentary: Chain of Demand applies AI, machine learning to retail supply chain profitability - FreightWaves - December 3rd, 2020
- Machine learning - it's all about the data - KHL Group - December 3rd, 2020
- Product Portfolio Analysis and Technological Development of Machine Learning in Medical Imaging Market during the forecasted period - Murphy's Hockey... - December 3rd, 2020
- Imaging AI and Machine Learning Beyond the Hype, Upcoming Webinar Hosted by Xtalks - PR Web - December 3rd, 2020
- Veritone aiWARE Now Supports NVIDIA CUDA for GPU-based AI and Machine Learning - Business Wire - December 3rd, 2020
- Exactech Launches Predict+, First Machine Learning-Based Software that Informs Surgeons with Patient-Specific Outcomes Predictions After Shoulder... - December 3rd, 2020
- How To Choose The Best Machine Learning Algorithm For A Particular Problem? - Analytics India Magazine - October 19th, 2020
- Lantronix Brings Advanced AI and Machine Learning to Smart Cameras With New Open-Q 610 SOM Based on the Powerful Qualcomm QCS610 System on Chip (SOC)... - October 19th, 2020
- AI and Machine Learning Technologies Expected to Play a Key Role in Expanding Multi Billion Dollar Digital Banking Sector: Report - Crowdfund Insider - October 19th, 2020
- AutoML Alleviates the Process of Machine Learning Analysis - Analytics Insight - October 19th, 2020
- Futurism Reinforces Its Next-Gen Business Commerce Platform With Advanced Machine Learning and Artificial Intelligence Capabilities - Yahoo Finance - October 19th, 2020
- Purebase Enhances Its Board of Advisors with An Expert on Machine Learning and Cheminformatics - GlobeNewswire - October 19th, 2020
- COVID-19 And The Role Of AI, Machine Learning In Logistics: A Conversation With Delhivery CTO Kapil Bharati - Mashable India - October 19th, 2020
- How to Beat Analysts and the Stock Market with Machine Learning - Knowledge@Wharton - October 19th, 2020
- AI and Machine Learning Can Help Fintechs if We Focus on Practical Implementation and Move Away from Overhyped Narratives, Researcher Says - Crowdfund... - October 19th, 2020
- Proximity matters: Using machine learning and geospatial analytics to reduce COVID-19 exposure risk - Healthcare IT News - September 20th, 2020
- PREDICTING THE OPTIMUM PATH - Port Strategy - September 20th, 2020
- What is 'custom machine learning' and why is it important for programmatic optimisation? - The Drum - September 20th, 2020
- How Machine Learning is Set to Transform the Online Gaming Community - Techiexpert.com - TechiExpert.com - September 20th, 2020
- Current and future regulatory landscape for AI and machine learning in the investment management sector - Lexology - September 20th, 2020
- Global Machine Learning Courses Market Research Report 2015-2027 of Major Types, Applications and Competitive Vendors in Top Regions and Countries -... - September 20th, 2020
- When AI in healthcare goes wrong, who is responsible? - Quartz - September 20th, 2020
- Is Wide-Spread Use of AI & Machine Intelligence in Manufacturing Still Years Away? - Automation World - September 20th, 2020
- How do we know AI is ready to be in the wild? Maybe a critic is needed - ZDNet - September 20th, 2020
- Solving the crux behind Apple's Silicon Strategy - Medium - September 20th, 2020
- Boost Your Animation To 60 FPS Using AI - Hackaday - September 20th, 2020
- 50 Latest Data Science And Analytics Jobs That Opened Last Week - Analytics India Magazine - September 20th, 2020
- Algorithms may never really figure us out thank goodness - The Boston Globe - September 20th, 2020
- Why Deep Learning DevCon Comes At The Right Time - Analytics India Magazine - September 20th, 2020
- Six notable benefits of AI in finance, and what they mean for humans - Daily Maverick - September 20th, 2020
- Twitter is looking into why its photo preview appears to favor white faces over Black faces - The Verge - September 20th, 2020
- 8 Trending skills you need to be a good Python Developer - iLounge - September 20th, 2020
- Automation Continuum - Leveraging AI and ML to Optimise RPA - Analytics Insight - September 20th, 2020
- UT Austin Selected as Home of National AI Institute Focused on Machine Learning - UT News | The University of Texas at Austin - August 27th, 2020
- Participation-washing could be the next dangerous fad in machine learning - MIT Technology Review - August 27th, 2020
- Getting to the heart of machine learning and complex humans - The Irish Times - August 27th, 2020
- Air Force Taps Machine Learning to Speed Up Flight Certifications - Nextgov - August 27th, 2020
- The Role of Artificial Intelligence and Machine Learning in the... - Insurance CIO Outlook - August 27th, 2020
- AI and Machine Learning Network Fetch.ai Partners Open-Source Blockchain Protocol Waves to Conduct R&D on DLT - Crowdfund Insider - August 27th, 2020
- AI may not predict the next pandemic, but big data and machine learning can fight this one - ZDNet - August 27th, 2020
- Machine Learning Artificial intelligence Market Size and Growth By Leading Vendors, By Types and Application, By End Users and Forecast to 2020-2027 -... - August 27th, 2020