Trevor Lanting, Chief Development Officer D-Wave shares the distinct advantages of annealing and gate-model quantum computing for various industries, emphasizing their roles in optimization, materials science, and AI. In this interview he talks about the potential for quantum computing to alleviate the computing demands in AI and ML across multiple sectors.

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Please share your journey to becoming Chief Development Officer (CDO) at D-Wave and what inspired your passion for quantum computing.

I have a background in physics, and I have always been interested in technology development. I am trained as an experimental physicist and my graduate work involved building superconducting instrumentation for microwave astronomy.

Through that training, I realized my passion really centered on developing technology and building tools. When D-Wave was recruiting for an experimental physicist in 2008, I jumped at the chance to join the team. Over the last 15 years, I have been involved with many aspects of our technology development, contributing directly to our annealing quantum computing development, our performance research program, and helping lead our software and algorithms teams. I was involved with work that was instrumental in demonstrating quantum entanglement in the fabric of our annealing processors, a major step in validating our technology approach.

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Several months ago, I stepped into a leadership role helping direct our overall research and product development efforts across software and hardware systems. I am incredibly excited about quantum computing. We are building technology that harnesses quantum mechanics to produce fundamentally new computational tools. As the technology rapidly matures, we are seeing a growing set of use cases that span from the acceleration of scientific discovery to optimization of complex business processes, and emerging machine learning applications.

Can you talk about the primary differences between D-Wave’s annealing and gate-model quantum computers, and how do these technologies benefit industries like AI, logistics, and materials sciences?

Annealing and gate-model quantum computing are two of the leading approaches to building practical large-scale quantum computing technology. These approaches offer distinct and complementary advantages for different use cases and applications, and we are developing both technology platforms.  

Annealing systems are uniquely suited for solving optimization problems. These problems, like tour scheduling, resource scheduling, and cargo loading, occur across many industries, like supply-chain logistics and manufacturing, and solving these problems leads to more efficient operations and direct cost savings.

 For materials sciences, gate-model systems have the potential to simulate the behavior of novel molecules and interactions between these molecules, promising to accelerate material discovery and drug design.

 For AI, annealing quantum computing can enhance machine learning algorithms, particularly in feature selection, model optimization, and providing rich quantum distributions that can be directly harnessed in generative AI architectures.  

Annealing quantum computing does have several advantages over current gate model systems: annealing protocols do not require significant pre-processing overheads associated with many gate-based protocols; annealing processor controls are continuously applied making the processors more resilient to errors and noise; and annealing processors are scaling to enterprise-level problem sizes more quickly. These characteristics make annealing quantum computing ideal for addressing today’s real-world challenges across industries.

How do you see the integration of quantum computing with AI and machine learning evolving, and what challenges and opportunities do you foresee?

It’s becoming apparent that the broader AI industry is facing a severe computing crunch. The amount of compute and the related energy costs needed to keep up with an increasing set of use cases is rapidly escalating. The industry should recognize that quantum computing technology might offer real opportunities to allow the industry to meet the growing demand for larger, more performant, and more energy efficient AI and ML architectures and workloads.

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 At the same time, we are in the early part of exploring how best to harness the power of quantum computing for AI. There is currently work in development here at D-Wave on using quantum distributions for designing modern generative AI architectures. This is an emerging field that involves directly using quantum processing unit samples that are not easily generated by classical computers, all of which could potentially improve how generative AI models are built.

 Customers such as TRIUMF, a Canadian physics lab, Honda Innovation Lab and Tohoku University, are already exploring D-Wave technology to address a variety of AI/ML workloads including pre-training optimization and more accurate and efficient model training.

 D-Wave has introduced a hybrid-quantum approach to optimizing feature selection in AI/ML model training and prediction. This approach is designed to help improve models’ accuracy by employing quantum systems to select the most representative dataset characteristics. Our partnership with Zapata AI continues to explore how the combination of quantum computing and generative AI could accelerate the development of new pharmaceuticals.

What are your predictions for the future of quantum computing, particularly in scalability, practical applications, and mainstream adoption across industries?

 For us at D-Wave, the future of quantum computing is firmly anchored in its practical applications. We’re already witnessing real-world impact across various industries, solving problems to directly improve people’s daily lives. Examples include quantum-optimized routes for grocery deliveries and more efficient supply-chain management. Overall, I expect quantum solutions to impact business operations by improving efficiencies in supply chain management, financial modeling, and resource allocation.

 Scalability is an important factor: the underlying quantum computing systems need to be designed for scalability from the beginning, and this a key reason why we focused our initial technology development effort on superconducting quantum annealing systems. As quantum computers become more powerful, growing in qubit count and quality, their ability to tackle larger and more complex problems will also increase.

 I believe quantum computing will play a meaningful role in drug discovery, accelerating the development of new medications and materials, and will be more broadly adopted across industries that face optimization challenges.

How do you think AI advancements will influence the evolution of quantum computing hardware and software solutions?

 We’ve talked about AI advancements making things faster and more scalable, and of course, this will allow for new discoveries. AI tools could also make quantum computing more accessible by automating some of the complex processes involved in quantum computations, problem formulation, solver parameter selection, and adding more user-friendly interfaces. This aligns with our goal at D-Wave, which is to make quantum computing a practical tool for solving real-world problems across various industries.

Could you share your thoughts on where you see AI, machine learning, and other smart technologies heading beyond 2024?

 In the future, I think we will see quantum-enhanced AI models outperform purely classical AI in many domains. Like any new emerging general-purpose technology, if we can put quantum computing, AI, and machine learning technologies in the hands of a broad and diverse set of users as fast as possible, unexpected and powerful use cases will quickly emerge and we will see these technologies embedded into our daily lives. And as domain experts in a wide range of fields adopt these powerful tools, progress on drug design, materials innovation and simulation, business optimization, and scientific discovery will accelerate. 

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

Seema Verma, EVP and GM, Oracle Health and Life Sciences, in this Q&A talks about strategic use of AI and technology to enhance healthcare efficiency, security, and patient care through innovations and collaborative partnerships and more about transforming healthcare by improving data interoperability.

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Hi Seema, welcome to our AiThority Interview series; walk us through your journey at Oracle and your learnings from being in the B2B SaaS industry

After spending four years as chief administrator of the Centers for Medicare & Medicaid Services (CMS), the largest payer in the world, I joined Oracle in April 2023 as senior vice president and GM of Oracle Life Sciences. In January 2024, I also became EVP and GM of Oracle Health, one of the largest EHR providers in the world, which marked a strategic shift for the company as we further strengthen the connection between these industries for the betterment of patients.

I’m about eight months into my new role, and there is still so much to be done as we continue to evolve our offerings, but I’m energized about the path forward. The opportunity to create a more effective, efficient, and secure healthcare system is immense – but achievable. What is unique about Oracle’s approach is that we are not just dipping our toe in the water. We have a breadth and depth of technology – the enterprise applications and cloud infrastructure, as well as the clinical trial applications and the EHR – that enables us to address the entirety of the industry’s challenges. We’re delivering leading technology and AI coupled with a long history of understanding how healthcare works to help health organizations worldwide reduce costs, increase efficiency, and improve patient care.

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Oracle is known for its autonomous database. How is this technology being utilized today within the health and life sciences sector to improve data security?

Oracle is the only healthcare technology provider that uses autonomous databases and operating systems to automatically patch and minimize any threat. At a time when cybercriminals are getting more sophisticated, this is game changing for the health and life sciences sector.

Given the impact that hacks can have on the delivery of care, cybersecurity should really be considered a subset of patient safety and receive similar levels of attention and investment.

The Oracle Autonomous Database is the first automated data management solution that helps eliminate cybersecurity’s biggest weakness – human error and delay. With our solution, healthcare organizations can take advantage of reliable security and compliance for sensitive data.

This includes leveraging AI and machine learning to transform operational reporting and get deeper insights into patients’ needs, helping enable quicker resolutions to critical health problems.

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How is Oracle leveraging AI to enhance interoperability and streamline care coordination among different healthcare providers?

Allowing patient data to flow with the patient through their healthcare journey, no matter where they are, is a priority for Oracle. To this end, we are making significant investments in interoperability and AI and doing so in a way that supports security and safety to create efficiency and cut costs in the healthcare system. With our complete suite for hospital automation, patient engagement systems, and everything in between, we are bringing all of these elements together: clinical, patient, enterprise, and data.

We’re embedding AI across all of these applications. This provides hospitals and life science companies with more intelligence and helps automate entire processes, enabling hospital systems to be more efficient, more secure, and more effective. Recent product innovations we’ve rolled out that are leveraging AI to help connect the healthcare ecosystem include:

• Oracle Health Data Intelligence, which stands at the forefront of the next generation of healthcare interoperability – capable of integrating data from over 2,000 sources and generating insights that are infused directly into the provider’s workflow to help close care gaps and improve quality measures.
• Oracle Clinical Digital Assistant, which takes advantage of generative AI to participate in patient appointments, capture patient-clinician interaction details, and draft structured clinical notes that doctors can quickly review and approve or edit. The offering dramatically reduces the amount of time clinicians spend interacting with the EHR and increases the amount of time they are focusing on patients. This is a game-changer that is helping to bring the joy back to practicing medicine for our customers.

Can you share some insights on the collaborative partnerships Oracle has formed to drive innovation in the healthcare sector?

We are committed to working with companies across the healthcare and life sciences industries to make healthcare more efficient, less expensive, and more effective. In some cases that means delivering new and deeply integrated APIs so customers can extract more value from our technology. For example, NetHealth (formerly known as Tissue Analytics), is focused on improving wound care documentation workflows. Its technology integrates advanced wound management and photo analysis directly within our EHR.

In other cases, we are enabling our partners to easily take advantage of Oracle Cloud Infrastructure (OCI) to deploy and scale their innovative healthcare offerings and services. For example, Imagene runs its AI inference on OCI, providing rapid AI-based molecular profiling from biopsy images to help transform cancer diagnosis and care.

We also join forces with like-minded organizations to expand our capabilities in accelerated computing and AI, so our customers and partners have access to the solutions they need to help solve the industry’s most complex and critical healthcare challenges. This includes enhancing our collaboration with Cohere to develop powerful, generative AI services for organizations worldwide. With Oracle’s comprehensive portfolio of cloud applications, data management expertise, and best-in-class AI infrastructure, combined with Cohere’s state-of-the-art large language models, we are deploying new models for healthcare and embedding generative AI throughout our healthcare-specific applications. We also team with NVIDIA to power healthcare solutions with accelerated computing in the cloud to help enable breakthroughs in research and advance the future of medicine.

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What are the key areas where AI can significantly impact drug pricing and pharmaceutical research, and how is Oracle’s product team contributing to these areas with new enhancements?

We expect AI to play a huge role in the life sciences industry. Obviously, the ability to recognize patterns and analyze vast amounts of data quickly is critical pharmaceutical research. The advances in AI are going to make it easier to pinpoint potential therapies faster and with greater accuracy. In turn, this will help companies to bring products to market more quickly and cost effectively. We are really just scratching the surface in terms of the impact AI will have on this industry. For example, we recently announced new capabilities in our AI-supported Oracle Argus and Safety One Intake solutions to help life science organizations meet evolving regulatory requirements and the rising volume of adverse event case workloads. And we are working to harness the full potential of AI to break down the silos between clinical care and research.

With your extensive experience in healthcare policy, how does Oracle ensure its AI solutions comply with stringent healthcare regulations and standards?

Policy experts around the world are grappling with how to regulate AI. It’s our job to work with our customers and comply with the regulations, which will vary by industry. With personal health information, there will be more conversations between medical providers and governments about how AI is applied. The requirements will likely be more extensive for healthcare than for something like menu management in the restaurant business. We will adjust the technology to meet those requirements and to make the technology easy to apply, even in highly regulated industries.

Before we conclude, can you talk about the future initiatives or advancements we can expect from Oracle in the healthcare and life sciences sector and how these efforts will continue to shape the landscape of healthcare technology?

So much of our work at Oracle is focused on putting patients first and supporting providers and caregivers by reducing the burden of tedious, manual administrative tasks that often overwhelm them and divert their time away from patients. This focus will continue to shape the future direction of our innovations and solutions across Oracle Health and Life Sciences.

The challenges facing our industry are vast and complex, but it’s also an incredibly exciting time. It’s a moment ripe for radical transformation that can propel us toward a genuinely value-based and more innovative healthcare system – the kind of healthcare system that we all want for ourselves, our families, and our communities. One where providers are measured based on the clinical outcomes they achieve and the cost of those results. Where teams of providers are encouraged to keep patients well rather than waiting for them to become sick. Where we connect clinical research to clinical care to get solutions to expedite solutions where none existed.

We have the technology to make this happen. We can create a healthcare system where we reduce the cost of administration, and those dollars can be reinvested into delivering cutting-edge treatments, improving care quality and accessibility and the health of the population. Seeing the work that Oracle is doing and the innovation that is coming from our team, I’ve never been more optimistic about the industry.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

Kunal Purohit, President – Next Gen Services, Tech Mahindra discusses several key initiatives and innovations at Tech Mahindra’s Makers Lab. He talks about Project Indus, an AI-driven enterprise platform that optimizes operational efficiency and focuses on the Hindi language and its dialects through a user-contribution portal in the following Q&A:

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Tech Mahindra’s Makers Lab is known for fostering innovation. Can you talk about some of the most impactful and disruptive solutions from Makers Lab recently?

At Tech Mahindra’s Makers Lab, our mission is to drive purpose-driven and human-centered innovation. One of our most impactful recent initiatives is Project Indus. Born out of a desire to revolutionize the future of work, Project Indus leverages the power of AI to create a seamless and intelligent enterprise platform. This platform optimizes operational efficiency and enhances decision-making processes by providing real-time insights and predictive analytics. Project Indus utilizes an innovative ‘GenAI in a box’ framework and simplifies the deployment of advanced AI models, making it easier for enterprises to integrate and scale AI applications. The initial phase of the Indus LLM targets the Hindi language and its 37+ dialects. The project includes a portal called projectindus.in, where users can contribute linguistic data.

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Our Makers Lab played a pivotal role in the conception and development of Project Indus. By fostering a collaborative environment, we combined the ingenuity of our diverse talent pool with cutting-edge technologies. The project aims to address real-world challenges faced by businesses today, offering scalable and adaptable solutions that drive growth and sustainability. Through Project Indus, Makers Lab exemplifies how innovation can be both disruptive and deeply beneficial, paving the way for smarter, more efficient enterprises.

What is your perspective on the impact of Generative AI (GenAI) on the workplace, particularly in terms of operational effectiveness and employee productivity?

GenAI is fundamentally transforming the workplace landscape, significantly enhancing operational effectiveness and employee productivity. At Tech Mahindra, we perceive GenAI as a catalyst for creating smarter, more efficient processes that drive innovation and deliver value at unprecedented speeds. We’ve introduced GenAI-driven pair programming to support our associates throughout the software development life cycle and have deployed GenAI-empowered co-pilots for boosting personal productivity.

By automating routine tasks, GenAI allows employees to focus on more strategic, creative endeavors, thereby boosting productivity and job satisfaction.

Our approach is holistic, focusing on empowering our workforce with advanced AI tools to foster sustainable growth and innovation. In an ever-evolving market, Tech Mahindra remains dedicated to creating a dynamic, agile workplace where technology and human ingenuity converge to deliver superior outcomes.

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What major ethical challenges do you foresee with integrating AI and quantum computing in the industry, and how is Tech Mahindra addressing them?

The integration of AI and quantum computing promises unparalleled advancements but also presents certain ethical challenges. One major concern is data privacy. Quantum computing’s immense processing power could potentially break current encryption methods, making sensitive data vulnerable. Additionally, the bias in AI algorithms can be magnified by the capabilities of quantum computing, leading to unintended and possibly discriminatory outcomes. At Tech Mahindra, we are proactively addressing these challenges through our Makers Lab initiatives. We are pioneering the development of quantum-safe cryptography to safeguard data in a post-quantum world. Moreover, our AI ethics framework emphasizes transparency, accountability, and fairness.

We are investing in interdisciplinary teams that include ethicists, technologists, and legal experts to ensure our innovations are aligned with ethical standards. By fostering a culture of ethical foresight and continuous learning, Tech Mahindra aims to lead responsibly in this transformative era, ensuring technology serves humanity’s best interests.

What significant AI tools and innovations has Makers Lab developed over the past few years?

At Makers Lab, our mission is to foster innovation by bridging the gap between imagination and reality. Over the past few years, we have harnessed the power of AI to create tools that push the boundaries of technology and deliver real-world impact. One of our standout innovations is the BHAML (Bharat Markup Language) solution that enables coding in native languages. Another remarkable creation is Enterprise Intelligence I/O (Entellio), a futuristic enterprise-grade on-premises chatbot powered by generative and discriminative AI. Some other innovations include Atmanirbhar Krishi, a super app for farmers, providing valuable agriculture related consolidated, curated information, and Panchang Intelligence, an ancient Indian almanac-based rainfall prediction solution.

Our commitment to quantum computing has also been recognized, with Avasant considering us a leading service provider in this cutting-edge field. Furthermore, our collaborative R&D efforts have earned us a place as a case study by the World Economic Forum, showcasing the impact of our innovative solutions. Our dedication to innovation has been acknowledged globally, with accolades such as the ISG Digital Case Study Award for Banking (UBI) in Metaverse 2023, Most Innovative Company 2021 and the Most Innovative Leader by the World Innovation Congress. Additionally, our support for start-ups was honored with the MindtheGap award for mentoring. At Makers Lab, we continue to drive technological advancements, making significant strides in the AI landscape.

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What emerging trends in AI and computing are you most excited about, and how is Makers Lab positioning itself to capitalize on them?

At Makers Lab, we are on the cusp of a revolution in AI and computing, eagerly embracing trends like GenAI, Quantum Computing, and Neuromorphic Engineering. The ability of GenAI to create content that mimics human creativity is reshaping industries from entertainment to healthcare. Quantum computing promises to solve problems beyond the reach of classical computers, potentially transforming everything from cryptography to complex system simulations. Neuromorphic engineering, with its brain-inspired architectures, offers a leap in efficiency and capability for AI systems.

Makers Lab is strategically positioned at the forefront of these innovations. Our multidisciplinary teams are developing quantum algorithms to accelerate machine learning, exploring the potential of neuromorphic chips for more efficient AI, and creating generative AI models that push the boundaries of creativity. By fostering a collaborative ecosystem, we are turning these emerging trends into practical solutions, ensuring that Tech Mahindra remains a leader in the next wave of technological advancement.

How do you ensure continuous learning and development within your team at Makers Lab?

We foster a culture of continuous learning by integrating experiential learning with a collaborative spirit. Our team engages in hands-on projects, exploring emerging technologies like AI, quantum computing, and blockchain. Regular knowledge-sharing sessions ensure that our learning ecosystem remains vibrant. For instance, we recently collaborated with a leading university on quantum algorithms, enabling our team to learn from top-tier researchers. In celebration of World Quantum Day 2024, Tech Mahindra and IQM Quantum Computers partnered to raise awareness, demonstrate, and promote the transformative power, and increase an understanding of quantum science and technology.

By encouraging curiosity and innovation, we stay ahead of technological trends and empower our team to drive groundbreaking solutions. This dynamic approach to learning transforms challenges into opportunities, fueling our mission to create a future-ready workforce.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

Dorian Selz, CEO of Squirro, talks about the recent advancements in generative AI capabilities, their acquisition of Synaptica, the impact of AI/ML technologies on the financial services industry and more in this Q&A:

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Hi Dorian, welcome to our AiThority Interview Series. As an experienced technology professional and an entrepreneur, can you outline the path that led you to your current role?

My career is a startup career. I’ve built businesses all my life. And I’ve had the privilege of serving on advisory boards for multiple organizations. In the 1990s, I was Partner and COO of Namics, the largest digital transformation consultancy in Switzerland and Germany, working with major clients like UBS and Siemens on their inaugural web pages.

Later I founded local.ch. We digitized the entire yellow page space and built Switzerland’s largest web property. And then in 2009, we launched Memonic which subsequently became Squirro.

My journey with pioneering tech-enabled solutions has culminated in my current venture, Squirro, where we’re tackling one of the most significant challenges: scaling enterprise data at scale and extracting value from unstructured data in enterprise settings.

Fast-forward to 2024. Squirro is a leading Swiss-headquartered global SaaS platform specializing in enterprise-ready generative AI, search, and business insights. We have dedicated teams in Switzerland, the United States, the UK, and Singapore with global reach and impact. 

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Squirro is recognized for its expertise in semantic search and AI. Could you elaborate on how Squirro’s platform integrates these technologies to provide actionable insights for enterprises?

At Squirro, we empower our clients with advanced AI technologies like SquirroGPT, which transforms vast amounts of data into actionable insights. Our dedication to providing indispensable enterprise AI solutions enables our clients to harness their data to drive growth and efficiency at scale by integrating search, insights, and automation, and addressing critical data security and privacy concerns. 

Squirro marries data from any source with the user intent and context to intelligently augment decision-making. Organizations across industries benefit from our scalable Insight Engine to unsilo, connect, and synthesize all data types and generate valuable, highly actionable information. 

Our Insight Engine is our core, the starting point for building vertical-specific Augmented Intelligence solutions. This creates a seamless and bespoke insights experience that helps everyone in the organization save time.

We are a leading contender because we have been building Insight Engines for the past decade and integrating machine learning techniques of all shapes, forms, and sizes. For us, integrating Large Language Models to provide one of the leading retrieval-augmented generation stacks has been a straightforward process. We serve customers across various domains, including the “Chat with My Data” use case. 

We’ve been recognized as the most visionary Insight Engine (Gartner Magic Quadrant for Insight Engines, 2021) and have developed quite a few innovative projects to expand the benefits of Augmented Intelligence, especially in fintech and regtech. 

Squirro’s platform integrates semantic search and AI to provide actionable insights for enterprises through several key mechanisms. By combining these technologies, Squirro transforms static information into decisive input factors for better and more intelligent business decision-making: 

Data Aggregation and Enrichment: Squirro connects and aggregates diverse internal and external data sources, handling massive data volumes. It enriches this content using Natural Language Processing (NLP) and machine learning (ML) techniques to discover, organize, and analyze data and content.

• Semantic Search and Knowledge Graphs: The platform utilizes semantic search and knowledge graphs to provide a deeper understanding of the data. This involves classifying content at a sentence level according to the user’s intent and linking concepts to other meaningful data, thereby extending search results and providing a comprehensive view.
• Advanced Data Analysis: Squirro’s AI platform focuses on unstructured data such as text, emails, news articles, and social media posts. By applying NLP and ML, it extracts valuable insights and patterns from this data, which would otherwise be challenging and time-consuming for humans to process.
• Real-time Actionable Insights: The platform delivers real-time, actionable insights through ready-to-use applications integrated into the digital workplace. This helps enterprises make smarter decisions daily by providing a 360-degree view of all enterprise sources.
• Service Management and Integration: Squirro’s Service Management Offering enhances customer support by providing service agents with instant access to historical data and suggested responses. It integrates with major business process management and service management platforms, ensuring smooth bidirectional communication.

Share details about some of Squirro’s recent product launches and their impact on your clients.

Squirro has developed a generative AI platform that employees can trust to deliver reliable and accurate insights, revolutionizing enterprise data management and setting new standards for responsiveness, accuracy, and customer satisfaction. By integrating technologies such as Retrieval Augmented Generation (RAG), evidence-based outputs, enterprise-ready security, semantic search, and unified data access, the platform ensures transparency, data privacy, and comprehensive organizational knowledge. Customization and compliance features further enhance its adaptability and regulatory adherence, while continuous learning ensures up-to-date insights.

The recent acquisition of Synaptica, a US-based SaaS provider of enterprise taxonomy management and knowledge graph systems, augments Squirro’s capabilities with robust semantic graph technology, facilitating knowledge discovery, conversational search, and business process automation. This combination of capabilities drives a new era of efficiency and innovation in customer support, ultimately leading to improved decision-making, increased productivity, and enhanced customer satisfaction.

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Squirro’s cutting-edge generative AI platform is  designed to revolutionize enterprise data management and redefine industry standards for responsiveness, accuracy, and customer satisfaction. The key capabilities that make Squirro’s solution reliable, accurate, and trustworthy for employees include:

• Retrieval Augmented Generation (RAG): Squirro combines Large Language Models (LLMs) with its proprietary Composite AI and Insight Engine technologies. This approach ensures that AI-generated responses are grounded in an organization’s own data, providing contextualized results, while minimizing hallucinations. 
• Evidence-based outputs: Every AI-generated claim is backed by auditable evidence, with sources linked to the LLM response. This transparency and explainability are crucial for building trust among users.
• Enterprise-ready security: Squirro’s GenAI solution is fully integrated across enterprise systems and can be deployed in an organization’s private cloud, ensuring data privacy and security.
• Semantic search capabilities: Squirro’s semantic enterprise search goes beyond traditional keyword-based search, understanding context, meaning, and relationships between words and concepts.
• Unified data access: The platform provides access to data from various sources and formats through over 100 enterprise data connectors, enabling a comprehensive view of organizational knowledge.
• Customization and personalization: Squirro’s AI adapts to specific industry needs and user preferences, offering personalized insights and recommendations.
• Compliance and governance: The solution is designed to meet strict regulatory requirements, with built-in access controls and audit trails.
• Continuous learning and adaptation: Squirro’s AI continuously refines its accuracy and relevance through ongoing learning, ensuring up-to-date and reliable insights.

Squirro works with several central banks and leading financial institutions. How is AI transforming the financial services industry, and what future developments do you foresee?

Squirro’s cutting-edge semantic search and generative AI platform capabilities  significantly impacted the financial services industry by enhancing data management, search capabilities, and decision-making processes. These innovations have earned Squirro a spot in the 4th Annual AIFinTech100 awards for 2024. 

• Integral to Squirro’s innovation in the knowledge management space is the use of Retrieval Augmented Generation (RAG), which streamlines the entire lifecycle of data from acquisition to deletion, ensuring swift, accurate collection, and efficient processing. This approach not only improves data integrity, security, and compliance but also significantly enhances the accessibility and relevance of information within large enterprises through advanced data indexing and hybrid search methodologies. Squirro’s RAG technology offers the implementation of stringent access control lists (ACLs) that maintains data security and compliance on a large scale. An Interactive chat interface and comprehensive system interaction also facilitates seamless user experiences and high-quality content generation and retrieval.

• Squirro’s generative employee agents have notably improved the effectiveness of 900 client advisors in asset management by providing seamless access to enterprise data and automating routine tasks. By offering enterprise-ready generative AI, search, business insights, and automation solutions, Squirro empowers financial institutions to harness the full potential of their data, leading to improved efficiency and productivity.

As AI continues to evolve, where do you see the technology heading beyond 2025, particularly in the context of its integration with industries such as finance and healthcare?

The Generative AI space is evolving at an unprecedented pace, and we are committed to providing a platform that empowers our customers to stay ahead. Large Language Models will have a more significant impact beyond chat applications. By combining the strengths of knowledge graphs and LLMs, we are unlocking new levels of efficiency and accuracy in customer support, allowing businesses to focus on what matters most – delivering value to their customers. We will continue to drive innovation, with our first addition being knowledge graph enhanced Retrieval-Augmented Generation (RAG) delivering  scalable enterprise data solutions in addition to  implementing robust practices and a proactive mindset that prioritizes the accuracy, consistency, and reliability of data. 

We have seen a strong acceleration of digital transformation. The way that we interact with each other, collaborate, share and consume information has changed fundamentally. Ideas and information are predominantly exchanged in the form of unstructured data. The sheer amount of information can be overwhelming, let alone the complexity due to the format and context.

With more and more data being generated, we have seen the fundamental need to make sense of the contained information and break it down into digestible pieces is growing.

In addition, the EU AI Act is set to take effect in January 2025 mandates that providers and deployers of AI systems ensure a sufficient level of AI literacy among their staff and other persons involved in the operation and use of AI systems. This requirement, outlined in Article 113(a), emphasizes the need for comprehensive AI literacy, considering technical knowledge, experience, education, and training and the context in which AI systems are used.

Therefore we will see a worldwide investment in AI literacy and compliance, ensuring data integrity in order to avoid fines, or risk non-compliance and facing penalties.

By prioritizing AI literacy and robust data management systems, businesses can safeguard their data and reputation, but failure to do so will result in devastating consequences, making the hidden costs of non-compliance a harsh reality. 

What are some emerging trends in AI and data analytics that you believe will shape the future?

As we look to the future, I’m excited about the potential of AI to reshape how businesses operate and make decisions. At Squirro, we’re not just observers of this change – we’re driving it, helping enterprises harness the full power of their data through advanced AI technologies.

How do you stay informed about the latest developments and innovations in AI and technology?

As Solution providers, we actively participate in vendor briefings to keep Gartner analysts informed about our latest products, services, and strategies in the rapidly evolving AI industry. The Vendor briefings serve as a conduit for sharing our innovations and market insights.           

Of course, our engagement with Gartner is not just a one-way street; it’s a dynamic feedback loop. We leverage Gartner’s market intelligence to refine our offerings and strategies, ensuring they align with industry trends and customer needs. Additionally, our partnerships  within our broader ecosystem allow us to tap into a diverse pool of knowledge and innovations in AI technology.

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This multi-faceted approach—combining our own developments, Gartner’s expert insights, and our partners’ innovations—enables us to maintain a comprehensive and up-to-date understanding of the AI landscape. By fostering these relationships and knowledge exchanges, we contribute to and benefit from a rich tapestry of industry expertise, driving continuous improvement and innovation in our AI solutions.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

Yair Amsterdam, CEO of Verbit, talks about the development of Verbit’s AI-powered solutions and highlights the emerging trends in AI and transcription, emphasizing the future potential of AI to understand context and drive meaningful actions from speech in real-time.

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Please tell us about your tech journey.

I have more than two decades in the global tech industry, working with companies like Verint, Ex Libris, Pro Quest and, now, Verbit. I started as a developer and took on more roles and responsibilities within those companies, serving as Chief Operating Officer at three different companies and currently serving as Chief Executive Officer at Verbit. I enjoy working for companies that do good and have a noble cause, like those focused on higher education and accessibility. It keeps me excited and makes me feel engaged.

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Can you highlight some of the solutions to the top operational challenges in integrating AI with human transcription services?

Automatic speech recognition (ASR) programs have been around for decades but, until recently, the quality of those programs have left a lot to be desired. Most ASR technologies can’t understand the unique terms, acronyms, slang and other sector-specific language used today, meaning most ASR-produced transcripts and captions are error-ridden and fall far short of their promise.

That’s why Verbit built its own ASR solution, complete with bespoke term boosting, dynamic domain dictionaries, and integrations into a host of platforms, to finally bring human-level accuracy to AI-generated transcripts and incubate a new sector called “verbal intelligence.”

As a leading provider of AI verbal intelligence, we set the standard for accuracy, efficiency and affordability, helping businesses, organizations and individuals of all sizes “make words work” by turning spoken audio and video into accessible and actionable text.

How is Verbit leveraging AI and human solutions to enhance its product offerings?

With the advent of generative AI, speech-to-text technology is rapidly advancing and Verbit is a major driver of this moment.

Earlier this year, we launched a new AI-powered solution ─ Captivate™ ─ that combines industry leading technology with decades of experience to provide high-accuracy transcripts and captions, guaranteed uptimes, customizable solutions, and integrations with multiple platforms. Built in-house by transcription, speech, and machine learning experts, Captivate is trained using diverse language models enabling it to understand languages, accents, and speech patterns better than generic ASR engines. The technology is scalable, can be tailored to the individual customer and applied to various ASR core models depending on their specific need.

In some cases, Verbit leans on its pool of expert human captioners to perform work that requires specific care. These same captioners are also utilized to train Verbit’s Captivate and prepare customized models for Verbit’s customers.

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Discuss the role of technology in maintaining Verbit’s competitive edge in the transcription industry.

Technology is at the core of our ability to maintain a competitive edge in the transcription industry. It’s an essential piece of the company’s DNA. It’s our technology, consistent accuracy and expertise that set us apart.

Our AI-powered, technology driven solutions help customers unlock the value of speech – making their words work by capturing and documenting them with our speech recognition technology, extracting additional insights, and quickly returning their value to enhance the messages that matter to you.

Our technology layers are augmented by human support to guarantee top-quality service. Combining human intelligence with our proprietary, continuously improving AI-based ASR​ engine provides the most accurate and reliable transcription solution on the market.

What are some emerging trends in the transcription and accessibility space that Verbit is focusing on?

More and more businesses are using artificial intelligence (AI) to keep up with their competition, with most citing increased efficiencies and automation among the driving forces pushing companies to employ advances in AI technology. And the legal sector is no different as lawyers and legal professionals are increasingly adopting AI to streamline the legal transcription processes, enhance accuracy and improve efficiency.

Verbit’s new Legal Real-Time offering, developed using our Captivate technology, is revolutionizing court reporting by providing accurate live transcription during depositions, hearings, arbitrations, examinations, trials and other legal proceedings. Our innovative solution provides increased efficiency to court reporters while offering real-time access to attorneys.

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What emerging trends in voice AI and human collaboration do you anticipate will shape the future of the industry?

We believe the next big thing in the AI productivity revolution will come from how we capture, understand and apply speech.

Our Captivate platform is the first step in our vision to create a world where speech can be seamlessly converted into meaningful actions, where words are captured, transcribed, reviewed, and saved, and where “actions” can be derived from those words in the moment. Understanding speech is the next frontier and where most major developments are now focused. Being able to understand context has come within reach with the dispersion of LLMs. Creating moments where AI can understand real intent, can provide suggestions in real-time on what question to ask or drive a group towards decisions is the next step. A world where insights aren’t distilled but revealed…instantaneously. That’s the future Verbit is building, where we truly “make words work.”

Before we wrap up, what advice would you give to aspiring leaders in the SaaS and technology sectors?

It’s important to have a clear, bold strategy – to recognize where you’ve been and know where you are leading to. Be able to articulate your vision to customers, employees and investors.

You can’t do it all by yourself, and it’s important to surround yourself with a strong team that helps you drive execution. Trust them to execute on the company’s visions and goals. Be flexible! The decisions you made yesterday likely will need to be slightly tuned and, sometimes, drastically changed.

But, perhaps most of all, enjoy the journey.

Thank you, Yair, for your insights; we hope to see you back on AiThority.com soon.

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