Babak Hodjat is CTO of AI at Cognizant, and former co-founder and CEO of Sentient. He’s liable for the core technology behind the world’s largest distributed artificial intelligence system. Babak was also the founding father of the world’s first AI-driven hedge fund, Sentient Investment Management. He’s a serial entrepreneur, having began plenty of Silicon Valley corporations as essential inventor and technologist.
Prior to co-founding Sentient, Babak was senior director of engineering at Sybase iAnywhere, where he led mobile solutions engineering. He was also co-founder, CTO and board member of Dejima Inc. Babak is the first inventor of Dejima’s patented, agent-oriented technology applied to intelligent interfaces for mobile and enterprise computing – the technology behind Apple’s Siri.
A broadcast scholar within the fields of artificial life, agent-oriented software engineering and distributed artificial intelligence, Babak has 31 granted or pending patents to his name. He’s an authority in quite a few fields of AI, including natural language processing, machine learning, genetic algorithms and distributed AI and has founded multiple corporations in these areas. Babak holds a Ph.D. in machine intelligence from Kyushu University, in Fukuoka, Japan.
Looking back at your profession, from founding multiple AI-driven corporations to leading Cognizant’s AI Lab, what are crucial lessons you’ve learned about innovation and leadership in AI?
Innovation needs patience, investment, and nurturing, and it needs to be fostered and unrestricted. Should you’ve built the best team of innovators, you possibly can trust them and provides them full artistic freedom to decide on how and what they research. The outcomes will often amaze you. From a leadership perspective, research and innovation shouldn’t be a nice-to-have or an afterthought. I’ve arrange research teams pretty early on when constructing start-ups and have all the time been a powerful advocate of research investment, and it has paid off. In good times, research keeps you ahead of competition, and in bad times, it helps you diversify and survive, so there isn’t a excuse for underinvesting, restricting or overburdening it with short-term business priorities.
As one among the first inventors of Apple’s Siri, how has your experience with developing intelligent interfaces shaped your approach to leading AI initiatives at Cognizant?
The natural language technology I originally developed for Siri was agent-based, so I even have been working with the concept for a very long time. AI wasn’t as powerful within the ’90s, so I used a multi-agent system to tackle understanding and mapping of natural language commands to actions. Each agent represented a small subset of the domain of discourse, so the AI in each agent had a straightforward environment to master. Today, AI systems are powerful, and one LLM can do many things, but we still profit by treating it as a knowledge employee in a box, restricting its domain, giving it a job description and linking it to other agents with different responsibilities. The AI is thus in a position to augment and improve any business workflow.
As a part of my remit as CTO of AI at Cognizant, I run our Advanced AI Lab in San Francisco. Our core research principle is agent-based decision-making. As of today, we currently have 56 U.S. patents on core AI technology based on that principle. We’re all in.
Could you elaborate on the cutting-edge research and innovations currently being developed at Cognizant’s AI Lab? How are these developments addressing the particular needs of Fortune 500 corporations?
We’ve several AI studios and innovation centers. Our Advanced AI Lab in San Francisco focuses on extending the state-of-the-art in AI. This is an element of our commitment announced last yr to take a position $1 billion in generative AI over the subsequent three years.
More specifically, we’re focused on developing latest algorithms and technologies to serve our clients. Trust, explainability and multi-objective decisions are among the many necessary areas we’re pursuing which can be vital for Fortune 500 enterprises.
Around trust, we’re curious about research and development that deepens our understanding of when we are able to trust AI’s decision-making enough to defer to it, and when a human should become involved. We’ve several patents related to any such uncertainty modeling. Similarly, neural networks, generative AI and LLMs are inherently opaque. We wish to give you the chance to guage an AI decision and ask it questions on why it beneficial something – essentially making it explainable. Finally, we understand that sometimes, decisions corporations need to give you the chance to make have a couple of final result objective—cost reduction while increasing revenues balanced with ethical considerations, for instance. AI can assist us achieve the perfect balance of all of those outcomes by optimizing decision strategies in a multi-objective manner. That is one other very necessary area in our AI research.
The following two years are considered critical for generative AI. What do you suspect will probably be the pivotal changes in this era, and the way should enterprises prepare?
We’re heading into an explosive period for the commercialization of AI technologies. Today, AI’s primary uses are improving productivity, creating higher natural language-driven user interfaces, summarizing data and helping with coding. During this acceleration period, we consider that organizing overall technology and AI strategies across the core tenet of multi-agent systems and decision-making will best enable enterprises to succeed. At Cognizant, our emphasis on innovation and applied research will help our clients leverage AI to extend strategic advantage because it becomes further integrated into business processes.
How will Generative AI reshape industries, and what are essentially the most exciting use cases emerging from Cognizant’s AI Lab?
Generative AI has been an enormous step forward for businesses. You now have the power to create a series of data staff that may assist humans of their day-to-day work. Whether it’s streamlining customer support through intelligent chatbots or managing warehouse inventory through a natural language interface, LLMs are superb at specialized tasks.
But what comes next is what is going to truly reshape industries, as agents get the power to speak with one another. The long run will probably be about corporations having agents of their devices and applications that may address your needs and interact with other agents in your behalf. They are going to work across entire businesses to help humans in every role, from HR and finance to marketing and sales. Within the near future, businesses will gravitate naturally towards becoming agent-based.
Notably, we have already got a multi-agent system that was developed in our lab in the shape of Neuro AI, an AI use case generator that enables clients to rapidly construct and prototype AI decisioning use cases for his or her business. It’s already delivering some exciting results, and we’ll be sharing more on this soon.
What role will multi-agent architectures play in the subsequent wave of Gen AI transformation, particularly in large-scale enterprise environments?
In our research and conversations with corporate leaders, we’re getting increasingly questions on how they will make Generative AI impactful at scale. We consider the transformative promise of multi-agent artificial intelligence systems is central to achieving that impact. A multi-agent AI system brings together AI agents built into software systems in various areas across the enterprise. Consider it as a system of systems that enables LLMs to interact with each other. Today, the challenge is that, despite the fact that business objectives, activities, and metrics are deeply interwoven, the software systems utilized by disparate teams should not, creating problems. For instance, supply chain delays can affect distribution center staffing. Onboarding a brand new vendor can impact Scope 3 emissions. Customer turnover could indicate product deficiencies. Siloed systems mean actions are sometimes based on insights drawn from merely one program and applied to 1 function. Multi-agent architectures will light up insights and integrated motion across the business. That’s real power that may catalyze enterprise transformation.
In what ways do you see multi-agent systems (MAS) evolving in the subsequent few years, and the way will this impact the broader AI landscape?
A multi-agent AI system functions as a virtual working group, analyzing prompts and drawing information from across the business to provide a comprehensive solution not only for the unique requestor, but for other teams as well. If we zoom in and take a look at a specific industry, this might revolutionize operations in areas like manufacturing, for instance. A Sourcing Agent would analyze existing processes and recommend less expensive alternative components based on seasons and demand. This Sourcing Agent would then connect with a Sustainability Agent to find out how the change would impact environmental goals. Finally, a Regulatory Agent would oversee compliance activity, ensuring teams submit complete, up-to-date reports on time.
The excellent news is many corporations have already begun to organically integrate LLM-powered chatbots, but they must be intentional about how they begin to attach these interfaces. Care should be taken as to the granularity of agentification, the sorts of LLMs getting used, and when and how you can fine-tune them to make them effective. Organizations should start from the highest, consider their needs and goals, and work down from there to come to a decision what could be agentified.
What are the essential challenges holding enterprises back from fully embracing AI, and the way does Cognizant address these obstacles?
Despite leadership’s backing and investment, many enterprises fear falling behind on AI. In response to our research, there is a gap between leaders’ strategic commitment and the arrogance to execute well. Cost and availability of talent and the perceived immaturity of current Gen AI solutions are two significant inhibitors holding enterprises back from fully embracing AI.
Cognizant plays an integral role helping enterprises traverse the AI productivity-to-growth journey. In reality, recent data from a study we conducted with Oxford Economics points to the necessity for outdoor expertise to assist with AI adoption, with 43% of corporations indicating they plan to work with external consultants to develop a plan for generative AI. Traditionally, Cognizant has owned the last mile with clients – we did this with data storage and cloud migration, and agentification will probably be no different. This is figure that should be highly customized. It’s not a one size suits all journey. We’re the experts who can assist discover the business goals and implementation plan, after which herald the best custom-built agents to deal with business needs. We’re, and have all the time been, the people to call.
Many corporations struggle to see immediate ROI from their AI investments. What common mistakes do they make, and the way can these be avoided?
Generative AI is much simpler when corporations bring it into their very own data context—that’s to say, customize it on their very own strong foundation of enterprise data. Also, in the end, enterprises can have to take the difficult step to reimagine their fundamental business processes. Today, many corporations are using AI to automate and improve existing processes. Greater results can occur once they begin to ask questions like, what are the constituents of this process, how do I alter them, and prepare for the emergence of something that does not exist yet? Yes, it will necessitate a culture change and accepting some risk, however it seems inevitable when orchestrating the various parts of the organization into one powerful whole.
What advice would you give to emerging AI leaders who want to make a big impact in the sphere, especially inside large enterprises?
Business transformation is complex by nature. Emerging AI leaders inside larger enterprises should concentrate on breaking down processes, experimenting with changes, and innovating. This requires a shift in mindset and calculated risks, but it may well create a more powerful organization.
