AiThority Interview with Manoj Saxena, Executive Chairman at CognitiveScale
Could you tell us about your journey in technology and how you started at CognitiveScale?
My initial passion for tech was sparked by the Mosaic browser in the mid-90s. With an Engineering degree under my belt and a new love for the internet, I left my job at 3M and took out 13 credit cards to start my own company. It was eventually acquired by CommerceOne, then my next company was acquired by IBM three years later. From there, I worked my way up to become IBM Watson’s first General Manager — and this is when I became deeply immersed in the world of Artificial Intelligence (AI).
I met a Co-Worker at IBM Watson Labs who ended up leaving to build Augmented Intelligence company CognitiveScale, and a year later, I also joined the team and have been involved for a little over five years now. As CognitiveScale’s Executive Chairman, my original passion for tech still lives on through the work we do building and deploying ethical and trusted AI for the enterprise.
Could you tell us about your daily interaction with new-age enterprise-level technologies like AI, Machine Learning and Robotics?
I interact with AI and Machine Learning (ML) daily at multiple different levels. From the moment I wake up, I’m going over my to-do list and reminders with Alexa or my Smart TV. Through these devices, I’m interacting with advanced technologies at a personal level. When I get to the CognitiveScale headquarters in the morning, I work with our clients every day to help them understand the art of the possible with AI and ML, including their advantages and perils alike.
As a part of my work on the CognitiveScale board and through my venture fund, I inform investors and board members about these technologies’ possibilities and limitations. Some days I visit the University of Texas at Austin, where I teach a course on designing responsible AI systems and inform students and faculty on the importance of embedding trust into new-age tech.
What are the basic differences between a generic AI and an Augmented intelligence platform?
At CognitiveScale, we focus on using Augmented Intelligence to pair humans and machines for the greater good. The basic differences between generic AI and Augmented Intelligence can be summed up in three parts.
- Focus: While generic AI typically deals with job of recognition and automation of tasks, Augmented Intelligence focuses on engaging, extending and amplifying human decision making and experiences.
- Goal: Augmented Intelligence puts the human at the center of its reason for being, with a concrete goal to contribute to the betterment of human society. On the other hand, the goal of generic AI is to solve tough, complex engineering or mathematical problems — which may not directly impact society.
- Domain: While Augmented Intelligence is industry-focused and needs to have knowledge about the specific domain it’s working in to learn, generic AI is broader and can span multiple sectors and industries.
What drives the AIOps engine at CognitiveScale? What kind of data warehousing do you have in place for AI-specific projects?
At CognitiveScale, our Cortex platform drives Augmented Intelligence predictions, applications and processes to deliver business value in a transparent and trusted manner. Cortex Certifai, our award-winning AI trust engine, focuses on making sure AI models are high-quality and transparent — and also achieves the following:
- Virtualizes and composes ML models along with traditional models or rule sets
- Personalizes and presents the right recommendations based on the specific user
- Explains itself in terms of why it generated a certain prediction
- Builds trust with customers, regulators and developers by informing them how and why the AI system is operating
Do you think AI has opened up “innovation gap” between technology providers and customers?
I’ve been working on this problem for eight years since I first joined IBM Watson, and I’ve noticed three significant gaps between the technology of AI versus the application of it.
- Education gap: The first step to building applications is understanding what AI is, but there is an overwhelming lack of education here. AI first, needs to be understood and explained through strategic business value.
- Implementation gap: With a set of algorithm-based models, the problem is that most of them operate as black boxes with no way to see what’s going on inside. According to Dimensional Research, eight out of ten AI projects actually stall from going into production due to the lack of data quality and labeling, but with interpretability and trust, these projects could get off the ground and into the market.
- Culture management gap: With technology dynamically evolving every day, we constantly need new talent and methods to adopt, govern, implement and evolve AI systems.
Which regions are showing the greatest interest in adoption of AI and related technologies? What do you think about the Chinese – Russian- America network in terms of market and innovations?
As Google’s CEO Sundar Pichai said, AI is more important now than electricity was years ago. More developed countries with better access to advanced technologies like the U.S., G7, China, and Russia are adopting AI much faster. But, we will continue to see this proliferate across all countries in time, as the good part about AI is that it’s transformative and will be adopted by all.
Would you agree that AI can successfully fill in for the lack of quality talent in the tech industry? For which human skills are leaders hiring and training?
While we’re a while away from using AI tutors to train people in AI, we’re currently in a five to seven-year period where we need to aggressively invest in education in both academia and the workforce alike. There’s a need and opportunity to approve the people we’re graduating, and training the next-gen workforce is why I became a professor at UT-Austin.
Data Science and Machine Learning skills are becoming standardized abilities that everyone should already have, but we’ll see the next two waves of hiring with more specific requirements. AI designers will become more desired among employers because they have a unique understanding of business problems and have domain expertise. Additionally, AI risk and compliance officers will be more in demand as AI systems continue to dynamically evolve. AI auditors will be more important than ever as transparency and trust increasingly reflect company values.
What is your opinion on “Weaponization of AI and Machine Learning”? How do you promote your ideas in the modern Digital economy?
People are now looking at AI and other advanced technologies as the next frontier of warfare. Autonomous warfare and the weaponization of AI have the potential to become a global problem, as some say the country with the most powerful AI will essentially rule the world in the future. While AI will drive innovation, it will also drive unintended consequences. We must create standards and put tools in place to ensure AI doesn’t become a detriment to society.
What digital technology start-ups and labs are you keenly following?
I’m always following the work and research put out by the large, established companies like Google, Microsoft, and IBM. Magic Leap is an upstart company that I’m particularly interested in. Upstarts are continually coming out with new business models and technologies for Augmented Reality, Virtual Reality, and Quantum Computing. I follow organizations that promote the use of ethical AI and are continuously on my radar, such as AI Global. I’m also impressed with the work the World Economic Forum has been doing in the transparent AI space.
What technologies within your industry are you interested in?
The list is endless. It’s never been a more exciting time to be an entrepreneur than now. With an explosion of tech and creativity, we’re beginning to see a new digital renaissance approach for the 2020s. To name a few, I’m interested in transparent and explainable AI as trust is the foundation of the digital economy, and until we solve this problem, we will not have adoption at scale. I’m also intrigued by the class of AI problems that Quantum Computing can solve. Lastly, some human-machine interface technologies are being produced that can connect your brain directly into a machine, so you don’t have to go through a keyboard. I’ve been following MIT’s work here.
As a tech leader, what industries you think would be fastest to adopting AI in car-making with smooth efficiency? What are the new emerging markets for these technology markets?
As AI continues to disrupt a variety of industries, there are two sectors we’re seeing adopt AI technologies faster than most. The transportation industry — whether it’s drones, cars, planes or trains — is arguably the only industry adopting AI at scale because it’s uniquely suited for applications of AI tech. On the other hand, information-intensive industries like Banking, Healthcare, Telecom, and Retail require an overwhelming wealth of data that can be efficiently processed and analyzed by AI systems. Because of this, we’re seeing these industries adopt AI at a rapid pace.
What’s your smartest work-related shortcut or productivity hack?
I love music, so I’ve hacked my Spotify app in a way that gives me a deeper set of music. I can now discover playlists that cross multiple regions to give me new inspiration throughout the day.
I have a brain that refuses to shut down, and at 3 AM or when I’m racing my car, ideas come to me. In these moments, I use Siri and Alexa to complete my to-do list and have it handy for me, so I don’t have to write an email to myself.
Tag the one person in the industry whose answers to these questions you would love to read
In this era, I’d be interested to see what it is that Thomas Edison would approach from a technological point of view as well as his thoughts on putting tech to work for good and using it to serve humanity. Additionally, Isaac Asimov’s perspective on Human-centric AI would be fascinating to read up on as he was the first person to think up the three laws of robotics and ethical AI.
Thank you, Manoj! That was fun and hope to see you back on AiThority soon.
Manoj Saxena is the Executive Chairman of CognitiveScale and a founding Managing Director of The Entrepreneurs’ Fund IV, a seed fund focused on B2B AI market with nine active investments.
Previously, he served as the first General Manager of IBM Watson, where his team built the first cognitive systems. Before IBM, Saxena successfully founded and sold two venture-backed software companies within five years.
He currently serves on the Board of AI Global, a non-profit dedicated to promoting practical and responsible applications of AI and the Saxena Family Foundation. Recently, Saxena retired after serving six years as Chairman of the US Federal Reserve Bank of Dallas, San Antonio.
CognitiveScale is an Enterprise AI software pioneer that pairs humans and machines to bring practical, scalable, trusted AI solutions to life. The company’s award-winning and proven Cortex software is deployed by leading global financial services, healthcare and digital commerce companies. The company is #1 in AI patents among privately held companies and #4 overall behind IBM, Google and Microsoft. Investors include Norwest Venture Partners, Intel Capital, IBM Watson, M12 (Microsoft Ventures) and USAA.