Erik Schwartz is the Chief AI Officer (CAIO) Tricon Infotech. a number one consulting and software services company. Tricon Infotech delivers efficient, automated solutions and full digital transformations through custom products and enterprise implementations
Erik Schwartz is a seasoned technology executive and entrepreneur with over twenty years of experience within the tech sector, specializing on the intersection of AI, Information Retrieval and Knowledge Discovery. Over the course of his profession, Erik has been on the forefront of integrating constructing large-scale platforms and integrating AI into search technologies, significantly enhancing user interaction and data accessibility. His previous held key senior roles at Comcast, Elsevier, and Microsoft, where he led pioneering AI, search, and LLM initiatives.
Erik’s skilled journey is marked by his dedication to innovation and his belief in the facility of collaboration. He has consistently driven teams towards the swift delivery of groundbreaking solutions, firmly establishing himself as a trusted leader within the technology community. His work, most recently on the Scopus AI project at Elsevier, underscores his commitment to redefining the boundaries of how we engage with information and create a trusted relationship with users.
In his role as Chief AI Officer (CAIO), Erik leverages his extensive experience to develop and implement comprehensive AI strategies for Tricon customers. His thorough process not only demystifies AI but in addition ensures that these businesses are equipped to succeed and thrive within the competitive landscape of AI technology. Erik is enthusiastic about fostering growth and innovation, sharing his insights to encourage and empower organizations to harness the transformative power of AI effectively.
Are you able to share some highlights of your profession journey that led to your current role as Chief AI Officer at Tricon Infotech?
I even have been immersed within the Information Retrieval domain throughout my entire profession. My journey began within the early 90s as a Web Master on the dawn of the Web. During this formative period, I focused on constructing digital libraries for presidency agencies, universities, and media corporations, which laid the muse for my expertise in digital information systems.
Within the 2000s, I transitioned to working with Search Engine vendors, where I honed my skills in search technologies. This phase of my profession was marked by significant growth and learning through various acquisitions, ultimately leading me to affix Microsoft in 2008. At Microsoft, I played a pivotal role in developing and enhancing Knowledge Discovery Platforms, driving innovation and improving information accessibility for users.
Following my tenure at Microsoft, I led initiatives at major corporations reminiscent of Comcast and Elsevier, where I used to be answerable for running large-scale Knowledge Discovery Platforms. These experiences have been instrumental in shaping my approach to AI and data retrieval, culminating in my current role as Chief AI Officer at Tricon Infotech. Here, I leverage my extensive experience to drive AI strategies and solutions that empower our clients to harness the complete potential of their data.
How have your experiences at corporations like Comcast, Elsevier, and Microsoft influenced your approach to integrating AI and search technologies?
Throughout my profession, I even have been deeply focused on natural language processing (NLP) techniques and machine learning. Initially, these technologies were based on simplistic rules-based systems. Nevertheless, as data sets grew larger and computing power became more robust, we began to significantly enhance user experiences by mechanically harvesting data and feeding it back into the algorithms to enhance their performance.
At Microsoft, following the acquisition of FAST, I served as a product manager on the SharePoint team. On this role, I used to be involved in integrating advanced search technologies into enterprise content management systems, enhancing information retrieval and collaboration capabilities for businesses.
At Comcast, I built a knowledge discovery platform that powered their entire video business, enabling users to go looking and discover content across set-top boxes, mobile, and web devices. This search engine scaled to handle over 1 billion requests per day, significantly improving the user experience by providing fast and accurate content recommendations and search results.
One of the crucial transformative experiences was at Elsevier, where we launched a Generative AI experience for Scopus, one in every of their most trusted products. This initiative utilized a Large Language Model (LLM) to help users in asking higher questions and obtaining more accurate answers from the deeply technical content within the scholarly communications database. This LLM-driven approach ensured the entire accuracy and trustworthiness of over 90 million articles contained throughout the database, demonstrating the facility of AI to reinforce academic research and knowledge dissemination.
What excites you probably the most concerning the current advancements in Generative AI and its potential applications?
Considered one of the largest historical challenges in Information Retrieval has been maintaining context. For humans, this can be a natural process, but for machines, finding information has traditionally been a really transactional experience: ask an issue, get a solution. Diving deeper right into a topic required asking increasingly specific questions. Generative AI revolutionizes this approach by enabling a more conversational and contextual interaction, very similar to a natural conversation with someone you’ve just met.
Moreover, Generative AI incorporates additional techniques that enhance deeper understanding, which have historically been difficult for traditional search engines like google. For instance, Large Language Models (LLMs) can seamlessly handle facets reminiscent of tone, sentiment evaluation, semantic understanding, and disambiguation. These capabilities allow LLMs to know the nuances of human language and context effortlessly, providing more accurate and meaningful responses right out of the box. This advancement excites me probably the most, because it opens up a myriad of possibilities for creating more intuitive, responsive, and intelligent applications across various domains.
How does Tricon Infotech’s approach to GenAI differ from other corporations within the industry?
Within the Generative AI space, there are two primary focus areas. The primary, which receives significant attention from a number of the largest technology vendors, is training and fine-tuning AI models. The second area, where Generative AI practitioners truly excel, is inference—using Generative AI to create helpful services.
At Tricon Infotech, we deal with the latter. Our approach is distinct because we emphasize practical application and rapid deployment. We’ve got developed a comprehensive program that helps business leaders quickly discover probably the most impactful use cases for Generative AI. Our process features a rapid prototyping solution, enabling customers to work with their very own data in an AI sandbox. This approach ensures that they will see tangible results and interact with AI-driven insights early in the event cycle.
Furthermore, now we have a radical deal with time-to-value. Our goal is to assist customers construct and deploy consumer-facing applications inside 90 days. This accelerated timeline not only drives faster innovation but in addition ensures that companies can quickly capitalize on the advantages of Generative AI, creating latest revenue streams and enhancing customer satisfaction.
Are you able to discuss a number of the key challenges in implementing Large Language Models (LLMs) and Generative AI in enterprise solutions?
Implementing Large Language Models (LLMs) and Generative AI in enterprise solutions presents several emerging challenges. The initially challenge is trust. Enterprises have to be assured that AI systems won’t compromise their mental property or sensitive corporate information. Ensuring data security and obtaining proper assurances that the AI won’t misuse data is critical for gaining trust.
The second challenge is the difficulty of hallucinations. Generative AI can sometimes produce confident answers which can be factually inaccurate. This may undermine the reliability of AI systems. Techniques reminiscent of fine-tuning models and employing Retrieval Augmented Generation (RAG) can assist mitigate the occurrence of hallucinations by ensuring that AI responses are grounded in accurate data.
The third significant challenge is cost. The licensing and scaling of LLMs could be quite expensive. Even enterprise offerings from major providers like Microsoft, Amazon, and Google include steep entry fees and minimums. Due to this fact, it’s crucial for enterprises to closely monitor and manage the return on investment (ROI) to be sure that the deployment of AI solutions is economically viable.
Are you able to explain the structured approach Tricon Infotech uses to develop customized GenAI enterprise solutions?
Tricon Infotech is a product development company that stands apart by offering managed services through dedicated, full-stack product teams reasonably than traditional staff augmentation. Our approach involves deploying entire product teams that may manage every aspect of the product development lifecycle, including user research, user experience design (UX), front-end and back-end development, test automation, deployment, scaling, and ongoing operations.
This comprehensive managed service model ensures that our customers can focus directly on capturing value from their data without the complexities and overhead of managing separate resources. Our key driver is time to value, meaning we prioritize delivering tangible advantages quickly and efficiently. Our ambition is to construct long-term generative relationships with our customers by continually adding value and iterating through the feature development process.
Our structured approach is designed to be agile and responsive, enabling us to adapt quickly to latest challenges and opportunities within the AI landscape. By leveraging the complete capabilities of our multidisciplinary teams, we deliver highly customized Generative AI solutions which can be tailored to the particular needs of every enterprise. This approach not only differentiates us from traditional staff augmentation firms but in addition ensures that we offer holistic, end-to-end solutions that drive significant business impact.
What are some examples of real-world problems that Tricon’s GenAI solutions have successfully addressed?
- E-Learning – converting traditional media and legacy educational material into interactive multi-modal content. This enables our customers to repurpose existing content to adapt to latest ways of learning and reach learners on different platforms where they already are. Further, the content can then be repurposed into hyper-personalized learning programs that may adapt mechanically to the learner’s needs and learning styles (audio, visual, etc.)
- Private AI – Helping customers construct trust enterprise AI solutions that remain private and honor customers access rule, while maintaining costs and helping to scale out across the assorted functions of the enterprise helping overburdened professionals and shared services scale out higher to the organization while natively understanding the assorted rules and restrictions of locale and regional policies distributed geographically. These private Ais won’t only serve the enterprise but will even generate latest streams of revenue for our customers.
- Process Automation – there are still a large variety of organizations who depend on manual processes and swivel chair data integration. AI helps to attach the assorted system together by creating intelligent layers that not only can validate data, but can understand the bespoke signal created by the unique dataset or tooling and help efficiently route workflows around while identifying supply chain issues
What role does continuous learning and growth play in staying ahead within the rapidly evolving field of AI?
One of the crucial significant challenges within the AI field is upskilling the talent pool. There may be a brand new generation of staff who intuitively understand AI tools and technologies. Nevertheless, there’s also an older generation that needs to know what these tools can and can’t do. Continuous learning is crucial for bridging this gap.
AI tools have the potential to dramatically enhance productivity, allowing businesses to realize rather more with significantly fewer resources, thereby reducing timeframes and costs. For these advantages to be realized, employees have to be open to learning latest ways of working and integrating these tools into their workflows.
Furthermore, addressing the fear of job security is important. Employees must understand that those that embrace continuous learning and growth will probably be higher equipped to include latest AI tools into their each day routines, ultimately resulting in greater job security. The fact is that success within the AI-driven future will come to those that actively seek to grasp and leverage these evolving technologies.
How do you envision the long run of AI transforming search technology and user interaction in the following decade?
We’re already witnessing a major shift from traditional search engines like google to Generative AI tools for initial queries. This shift is driven by the flexibility of Generative AI to supply direct answers and solutions, eliminating the necessity to traverse multiple web sites or resources independently. Within the near future, it can develop into commonplace for AIs to attend meetings, take actions, and handle routine tasks, resulting in a considerable reduction within the roles of certain functions inside enterprises.
Considered one of the important thing challenges that continues to be is determining monetize Generative AI, as the standard promoting model may face significant hurdles on this latest landscape. My prediction is that data will develop into increasingly helpful, acting more like a currency as we navigate this brave latest world. This shift would require progressive business models that leverage the unique capabilities of AI while ensuring that users and enterprises can derive tangible value from their interactions.
Overall, the long run of AI in search technology and user interaction guarantees to be transformative, making information retrieval more intuitive and efficient while reshaping the way in which we approach digital interactions and enterprise functions.
What practical advice would you give to businesses seeking to leverage AI to drive success and innovation?
Don’t be afraid of the technology. Start by making AI tools available to your employees to be sure that your data and mental property (IP) remain secure. Many employees are already using AI tools, but without proper governance, there’s a risk of misuse. Due to this fact, it’s crucial to upskill your staff in order that they understand the risks involved and use these tools safely and effectively.
Moreover, it is important to pay close attention to the measures of success. AI tools could be expensive, but the prices are expected to diminish over time. Nevertheless, it is crucial to maintain a transparent deal with the return on investment (ROI) to administer costs and understand the impact on your online business. By doing so, you may leverage AI to drive innovation and success while ensuring that the advantages outweigh the expenses.
