Trends and risks in a Golden Age of Artificial Intelligence and Machine Learning
By David Churbuck, Founder & former Editor-in chief of Forbes.com
NEW YORK, Sept. 5, 2022 /PRNewswire/ -- Over the summer of 2022, Wow AI, a global provider of high-quality AI training data based in New York City, invited a panel of experts from different industries and areas of expertise to share their insights into the current state of artificial intelligence and machine learning (AI/ML) and discuss the factors that have accelerated the recent adoption of AI in applications.
Trends and risks in a Golden Age of Artificial Intelligence and Machine Learning will be discussed at Worldwide AI Webinar.
All the experts agreed that the past decade has been a Golden Age for AI, made possible by the affordable availability of AI services delivered from the cloud and the inexpensive power of graphics processing units designed to handle the types of transforms and calculations at the foundation of AI models.
However, they each have a unique perspective on different trends and issues as AI pervades society, continuously improving the human-machine interface and becoming more embedded in every aspect of our lives.
The Experts
The five experts will share more insights along with more than 20 other thought leaders in AI/ML recruited from Fortune 500 companies and organizations around the world such as Walt Disney, Deloitte, Microsoft, Oxford Brookes University, The US Department of Commerce and many others, during a two-day online discussion of contemporary AI and ML trends on September 29-30 hosted by Wow AI.
Welcome and thanks for joining. There are fears about an AI going out of control – such as Skynet or Hal 9000 or devices like Amazon Alexa or Google Assistant or Apple Siri. 75 years later, there is legislation pending at the state and federal levels to regulate AI and review algorithms for signs of bias or the perpetuation of old models that could deny a person equal opportunity. What is the risk the gains of the past ten years could be reversed or future developments hindered by fear, baseless conspiracy theories, or over-regulation?
Andreas: I think if we look at people and humanity as a whole, there has always been a fear of not being the pinnacle of evolution. You need to make sure that the people who are affected by the change are part of the process, that they are aware of why and how you want to introduce a piece of technology like AI, what the limitations are, and where it can help them become better and more effective.
Noelle: There are more threatening devices than Alexa. The average smartphone has 50 applications trying to get permission to access our camera, microphone, and contacts. I've consistently been opposed to AI being applied to anything demographically oriented. [...] Biases end up perpetuating bad behavior. Maybe the models need to be infused with some inclusivity."
In the 1980s AI seemed to have potential in decision support systems but then it seemed to stall. Then, almost overnight it was in our cars, our phones, and our living rooms, to the point where we're looking at autonomous vehicles, real-time meeting transcription, and in the case of Aravind's company, Uniphore, analyzing customer interactions for tone and emotion. What happened that helped AI get over the hype that surrounded it in the past while delivering significant results after so many years of being ignored?
David Von Dollen: I would say two factors brought AI out of its "winter". One was hardware – computing power primarily in the form of GPUs which have had a tremendous impact. The other factor is ongoing refinements to the underlying algorithms.
Patrick Bangert: This renaissance of AI we are experiencing today is sometimes called the "Deep Learning Revolution." Yes, some of it comes down to processing speed and we have the graphics processing units we didn't have 30 years ago, but it's not just about speed. Speed is mainly interesting and beneficial in the sense that it allows us to train much bigger models in the same amount of time. The second benefit is scientific. A lot of headway is being realized in deep learning due to the mathematics of AI gaining novel algorithms and modeling methods that are better than what we had in the 1980s.
Aravind Ganapathiraju: The difference is accuracy. The first ASR system (automated speed recognition) had a 40% error rate. On the same task today we are pushing a 5% error rate. I'm not saying it's a solved problem, but it is indicative of the evolution that has happened over the last two decades.
Let's talk about the role data has played in helping AI/ML deliver on its promises. Aside from strict laws governing the processing and storage of personal data and regulations to ensure data privacy, what should providers of AI-enabled products and services be thinking about when it comes to data?
Aravind: One of the latest products we have released at Uniphone is "Q for Sales" which analyzes conversations not just by examining the tonal information in a call such as this one, but also by other visual cues. The fusion of different data types – from audio to text, from video to facial expressions – provides a call center person with valuable insights and nudges to gain a better outcome from the call.
Andreas Welsch: A byproduct of the early 2000s' Big Data trends has been an influx of so many data points that it's not possible for one individual or even a team of five or ten data scientists to analyze at the speed, scale, and quality needed to make decisions in business today. With the application of AI on the task, we're able to detect these patterns in the data that allow you to automate certain parts of your business processes in a way that has never been possible before.
I also think, to Aravind's point, that there are just so many more data pools available and now we have the tools to analyze them on a much larger scale than ever before.
Patrick Bangert: At Samsung, we train all sorts of models. [...] The role data plays across the company is driving AI systems to forecast how many people will buy a particular Samsung device, at which stores, and how to get inventory to those stores upon launch. Our internal data is the fuel for those forecasting systems, data unique to our business and our success.
Noelle Silver: I think Web3 is really forcing people to rethink data and there's an ethical shift between the collectors of data and the sources of that data. Companies like Apple distinguish themselves by popping up a little dialogue that asks "Do you want to share your real address or would you like us to mask it for you?" That translates in my mind to more responsibility on the part of the companies to be responsible, ethical stewards of their users' data.
David Von Dollen: I focus a lot on what I call "narrow AI" – an algorithm that's trained on a specific set of data to perform a specific task. That's what a lot of our applications do today. It's all pretty much pattern recognition but within narrowly defined constraints. I think those types of applications may turn out to be much more harmful than some sentient AI taking over in a Skynet situation.
To watch the full conversation with the experts who will be keynoting the Worldwide AI Webinar, please visit:
These five thought leaders and other experts from around the world will be taking your questions, and discussing the issues and opportunities in AI and ML applications, training models, and data sources, and other topics at Worldwide AI Webinar on September 29 and 30th.
Media Contact: David Churbuck - Founder & former editor-in-chief of Forbes, a prize-winning tech journalist - David@churbuck.com
