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Joy Mustafi, Chief Data Scientist of Aviso, Inc – Interview Series

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Ranked as one of India’s 10 top data scientists by Analytics India Magazine,  Joy Mustafi has led data science research at tech giants including Salesforce, Microsoft, and IBM, winning 50 patents and authoring over 25 publications on AI.

He was associated with IBM for a decade as Data Scientist involved in a variety of business intelligence solutions, including IBM Watson. He worked as Principal Applied Scientist at Microsoft, responsible for AI research. Most recently, Mustafi was the Principal Researcher for Salesforce’s Einstein platform.

Mustafi is also the Founder and President of MUST Research, a non-profit organization promoting excellence in the fields of data science, cognitive computing, artificial intelligence, machine learning, and advanced analytics for the benefit of society.

Recently Mustafi joined Redwood City-based Aviso, Inc as Chief data scientist, where he will leverage his decades of experience to help Aviso customers accelerate deal-closing and expand revenue opportunities.

What initially attracted you to AI?

I love mathematics a lot, and the same for programming. I did my graduate degree in statistics and post-graduate work in computer applications. When I started my AI research journey back in 2002 at the at Indian Statistical Institute in Kolkata, I used the C programming language to develop an Artificial Neural Network system for handwritten numeral recognition. That was 2500+ lines of code, all written from scratch without any inbuilt libraries apart from standard input / output. It consisted of data cleansing and pre-processing, feature engineering, and a back propagation algorithm with a multilayer perceptron. The entire process was a combination of all the subjects that I studied. At that time AI was not so popular in the corporate world, and few academic organisations were doing advanced research in the field. And, by the way, AI wasn’t new at the time! The field of AI research dates all the way back to 1956, when Prof. John McCarthy and others inaugurated the field at a now-legendary workshop at Dartmouth College.

 

You have worked with some of the most advanced companies in AI such as IBM Watson & Microsoft. What has been the most interesting project that you have worked on? 

I want to mention the first patent I was awarded while working at IBM: a  method for solving word problems in natural language, which was an open problem with IBM Watson. The system I developed can understand an arithmetic or algebraic problem stated in natural language and provide a solution in real-time as a natural language answer. To do that, the system had to handle the following key steps: Get the input problem statements and question to be answered; convert the input sentences to a sequence of sentences which are well-formed from a mathematical perspective; convert the well-formed sentences into mathematical equations; solve the set of equations; and narrate the mathematical result in natural language.

There’s also my best project for Microsoft — Softie! I invented and built a physical robot equipped with various types of interchangeable input devices and sensors to allow it to receive information from humans.  A standardized method of communication with the computer allowed the user to make practical adjustments, enabling richer interactions depending on the context. We were able to implement a robust system with features including a keyboard, pointing device, touchscreen, computer vision, speech recognition, and so forth. We formed a team from various business units, and encouraged them to explore research applications on artificial intelligence and related fields.

 

You’re also the Founder and President of MUST Research, a non-profit organization registered under Society and Trust Act of India. Could you tell us about this non-profit?

MUST Research is dedicated to promoting excellence and competence in the fields of data science, cognitive computing, artificial intelligence, machine learning, and advanced analytics for the benefit of the society. MUST aims to build an ecosystem to enable interaction between academia and enterprise, helping them to resolve problems and making them aware of the latest developments in the cognitive era to provide solutions, offer guidance or training, organize lectures, seminars and workshops, and collaborate on scientific programs and societal missions. The most exciting feature of MUST is its fundamental research on cutting-edge technologies like artificial intelligence, machine learning, natural language processing, text analytics, image processing, computer vision, audio signal processing, speech technology, embedded systems, robotics, etc.

 

What was it that inspired you to launch MUST Research?

My love of sci-fi movies and mathematics means I’m often thinking about how technology can change the world, and I’d been thinking about forming a group of like-minded experts on advanced technologies since 1993, when I was in 9th grade. Once I got my first job, it took 10 years to call for a meeting — and another 10 years to identify a group of suitable experts and form a non-profit society. Now, though, we have around 500 data scientists in MUST across India who are passionately contributing to research on emerging technologies.

 

Over the past several years the industry has been significant advances in deep learning, reinforcement learning, natural language processing, etc. Which area of machine learning do you currently view as the most exciting?

All machine-learning algorithms are exciting once they are implemented as a product or service that can be used by businesses or individuals in the real world. The Deep Learning era has pros and cons, though — sometimes it helps in automatic feature engineering, but at the same time it can work like a black box, and end up with a garbage-in-garbage-out scenario if proper datasets or algorithms aren’t used. Some of the latest technologies are also resource-hungry and require huge amounts of processing power, time, and data. The key thing to remember is that Deep Learning is a subset of Machine Learning (ML), which in turn is a subset of Artificial Intelligence (AI), and AI is a subset of Data Science — so it’s all connected. And it’s not about Python, R or Scala — I started my AI journey in C, and one can even write AI programs in assembly language code. Building successful AI systems depends first and foremost on understanding the business or research environment, and then connecting the dots between actions and data to build a system which genuinely helps various people in different domains. Whether you’re working with  Natural Language Processing, Computer Vision, Video Analytics, Speech Technology, or Robotics, the best way forwards is to start with the simplest possible approach, and then adopt more complex methods iteratively as you experiment with and refine your system.

 

You are a frequent guest speaker at leading universities in India. What is one question that you often hear from students, and how do you best answer it?

The single question I hear most often is: “How can I become a data scientist?”  I always tell young people that it’s definitely possible, and try to guide them towards using their love of mathematics, statistics, or computer science to try to solve real-world business problems. People also  ask how they can join MUST, and again, the answer  is simple: “Build your profile with multiple projects and focus on thinking outside of the box.” If you want to become a data scientist, you have to also prove that you can innovate. Without innovation, we can’t call ourselves scientists. Of course, being awarded patents or publishing your research in reputed journals and conferences also helps!

 

You recently joined Redwood City-based Aviso as chief scientist, in order to use your AI/ML expertise. Could you tell us a bit about Aviso and your role with this company?

Aviso uses AI and machine learning to guide sales executives and take the guesswork out of the deal-making process. That’s a fascinating challenge, and my primary responsibility is to help the organization grow in a positive direction, using deep research to set the stage for the customers’ success. I’m using my knowledge and experience in artificial intelligence and innovation to help make our core products and research projects more:

Adaptive: They must learn as information changes, and as goals and requirements evolve. They must resolve ambiguity and tolerate unpredictability. They must be engineered to feed on dynamic data in real time.

Interactive: They must interact easily with users so that those users can define their needs comfortably. They must interact with other processors, devices, services, as well as with people.

Iterative and Stateful: They must aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They must remember previous interactions in a process and return information that is suitable for the specific application at that point in time.

Contextual: They must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulation, user profile, process, task and goal. They must draw on multiple sources of information, including both structured and unstructured digital information.

 

What was it that attracted you to this position with Aviso?

Aviso is working to replace bloated legacy CRM systems with frictionless, AI-enabled tools that can deliver actionable insights and unlock sales teams’ full potential. Our product is a smart system which understands the pain points of salespeople, does away with time-consuming data entry, and gives executives the suggestions and guidance they need to close deals effectively. I was attracted to the strong leadership team and customer  base, but also to Aviso’s commitment to using sophisticated AI tools to solve real-world challenges. Selling is a vital part of any business, and Aviso helps with that by leveraging the power of artificial intelligence. Bulls-eye! What more could you want?

 

Lastly, is there anything else that you would like to share about AI?

Artificial intelligence makes a new class of problems computable. To respond to the fluid nature of users understanding of their problems, the cognitive computing system offers a synthesis not just of information sources but of influences, contexts, and insights. These systems differ from current computing applications in that they move beyond tabulating and calculating based on pre-configured rules and programs. They can infer and even reason based on broad objectives. In this sense, cognitive computing is a new type of computing with the goal of developing more accurate models of how the human brain or mind senses, reasons, and responds to stimulus. It is a field of study which studies how to create computers and computer software that are capable of intelligent behavior. This field is interdisciplinary: artificial intelligence is a place where a number of sciences and professions converge, including computer science, electronics, mathematics, statistics, psychology, linguistics, philosophy, neuroscience, and biology. That’s what makes it so exciting!

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Antoine Tardif is a Futurist who is passionate about the future of AI and robotics. He is the CEO of BlockVentures.com, and has invested in over 50 AI & blockchain projects. He is the Co-Founder of Securities.io a news website focusing on digital securities, and is a founding partner of unite.AI. He is also a member of the Forbes Technology Council.

Autonomous Vehicles

Andrew Stein, Software Engineer Waymo – Interview Series

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Andrew Stein is a Software Engineer who leads the perception team for Waymo Via, Waymo’s autonomous delivery efforts. Waymo is an autonomous driving technology development company that is a subsidiary of Alphabet Inc, the parent company of Google.

What initially attracted you to AI and robotics?

I always liked making things that “did something” ever since I was very young. Arts and crafts could be fun, but my biggest passion was working on creations that were also functional in some way. My favorite parts of Mister Rogers’ Neighborhood were the footage of conveyor belts and actuators in automated factories, seeing bottles and other products filled or assembled, labeled, and transported. I was a huge fan of Legos and other building toys. Then, thanks to some success in Computer Aided Design (CAD) competitions through the Technology Student Association in middle and high school, I ended up landing an after-school job doing CAD for a tiny startup company, Clipper Manufacturing. There, I was designing factory layouts for an enormous robotic sorter and associated conveyor equipment for laundering and organizing hangered uniforms for the retail garment industry. From there, it was off to Georgia Tech to study in electrical engineering, where I participated in the IEEE Robotics Club and took some classes in Computer Vision. Those eventually led me to the Robotics Institute at Carnegie Mellon University for my PhD. Many of my fellow graduate students from CMU have been close colleagues ever since, both at Anki and now at Waymo.

You previously worked as a lead engineer at Anki a robotics startup. What are some of the projects that you had the opportunity to work on at Anki?

I was the first full-time hire on the Cozmo project at Anki, where I had the privilege of starting the code repository from scratch and saw the product through to over one million cute, lifelike robots shipped into people’s homes. That work transitioned into our next product, Vector, which was another, more advanced and self-contained version of Cozmo. I got to work on many parts of those products, but was primarily responsible for computer vision for face detection, face recognition, 3D pose estimation, localization, and other aspects of perception. I also ported TensorFlow Lite to run on Vector’s embedded OS and helped deploy deep learning models to run onboard the robot for hand and person detection.

I also built Cozmo’s and Vector’s eye rendering systems, which gave me the chance to work particularly closely with much of Anki’s very talented and creative animation team, which was also a lot of fun.

In 2019, Waymo hired you and twelve other robotics experts from Anki to adapt its self-driving technology to other platforms, including commercial trucks. What was your initial reaction to the prospect of working at Waymo?

I knew many current and past engineers at Waymo and certainly was aware of the company’s reputation as a leader in the field of autonomous vehicles. I very much enjoyed the creativity of working on toys and educational products for kids at Anki, but I was also excited to join a larger company working in such an impactful space for society, to see how software development and safety are done at this organizational scale and level of technical complexity.

Can you discuss what a day working at Waymo is like for you?

Most of my role is currently focused on guiding and growing my team as we identify and solve trucking-specific challenges in close collaboration with other engineering teams at Waymo. That means my days are spent meeting with my team, other technical leads, and product and program managers as we plan for technical and organizational approaches to develop and deploy our self-driving system, called the Waymo Driver, and extend its capabilities to our growing fleet of trucks. Besides that, given that we are actively hiring, I also spend significant time interviewing candidates.

What are some of the unique computer vision and AI challenges that are faced with autonomous trucks compared to autonomous vehicles?

While we utilize the same core technology stack across all of our vehicles, there are some new considerations specific to trucking that we have to take into account. First and foremost, the domain is different: compared to passenger cars, trucks spend a lot more time on freeways, which are higher-speed environments. Due to a lot more mass, trucks are slower to accelerate and brake than cars, which means the Waymo Driver needs to perceive things from very far away. Furthermore, freeway construction uses different markers and signage and can even involve median crossovers to the “wrong” side of the road; there are freeway-specific laws like moving over for vehicles stopped on shoulders; and there can be many lanes of jammed traffic to navigate. Having a potentially larger blind spot caused by a trailer is another challenge we need to overcome.

Waymo’s recently began testing a driverless fleet of heavy-duty trucks in Texas with trained drivers on-board. At this point in the game, what are some of the things that Waymo hopes to learn from these tests?

Our trucks test in the areas in which we operate (AZ / CA / TX / NM) to gain meaningful experience and data in all different types of situations we might encounter driving on the freeway. This process exercises our software and hardware, allowing us to learn how we can continue to improve and adapt our Waymo Driver for the trucking domain.

Looking at Texas specifically: Dallas and Houston are known to be part of the biggest freight hubs in the US. Operating in that environment, we can test our Waymo Driver on highly dense highways and shipper lanes, further understand how other truck and passenger car drivers behave on these routes, and continue to refine the way our Waymo Driver reacts and responds in these busy driving regions. Additionally, it also enables us to test in a place with unique weather conditions that can help us drive our capabilities in that area forward.

Can you discuss the Waymo Open Dataset which includes both sensor data and labeled data, and the benefits to Waymo for sharing this valuable dataset?

At Waymo, we’re tackling some of the hardest problems that exist in machine learning. To aid the research community in making advancements in machine perception and self-driving technology, we’ve released the Waymo Open Dataset, which is one of the largest and most diverse publicly available fully self-driving datasets. Available at no cost to researchers at waymo.com/open, the dataset consists of 1,950 segments of high-resolution sensor data and covers a wide variety of environments, from dense urban centers to suburban landscapes, as well as data collected during day and night, at dawn and dusk, in sunshine and rain. In March 2020, we also launched the Waymo Open Dataset Challenges to provide the research community a way to test their expertise and see what others are doing.

In your personal opinion, how long will it be until the industry achieves true level 5 autonomy?

We have been working on this for over ten years now and so we have the benefit of that experience to know that this technology will come to the world step by step. Self-driving technology is so complex and we’ve gotten to where we are today because of advances in so many fields from sensing in hardware to machine learning. That’s why we’ve been taking a gradual approach to introduce this technology to the world. We believe it’s the safest and most responsible way to go, and we’ve also heard from our riders and partners that they appreciate this thoughtful and measured approach we’re taking to safely deploy this technology in their communities.

Thank you for the great interview, readers who wish to learn more should visit Waymo Via.

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Interviews

Michael Schrage, Author of Recommendation Engines (The MIT Press) – Interview Series

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Michael Schrage is a Research Fellow at the MIT Sloan School of Management’s Initiative on the Digital Economy. A sought-after expert on innovation, metrics, and network effects, he is the author of Who Do You Want Your Customers to Become?The Innovator’s Hypothesis: How Cheap Experiments Are Worth More than Good Ideas (MIT Press), and other books.

In this interview we discuss his book “Recommendation Engines” which explores the history, technology, business, and social impact of online recommendation engines.

What inspired you to write a book on such a narrow topic as “Recommendation Engines”?

The framing of your question gives the game away…..When I looked seriously at the digital technologies and touchpoints that truly influenced people’s lives all over the world, I almost always found a ‘recommendation engine’ driving decision. Spotify’s recommenders determine the music and songs people hear; TikTok’s recommendation engines define the ‘viral videos’ people put together and share; Netflix’s recommenders have been architected to facilitate ‘binge watching’ and ‘binge watchers;’ Google Maps and Waze recommend the best and/or fastest and/or simplest ways to get there; Tinder and Match.com recommend who you might like to be with or, you know, ‘be’ with; Stitch Fix recommends what you might want to wear that makes you ‘you;’ Amazon will recommend what you really should be buying; Academia and ResearchGate will recommend the most relevant research you should be up to date on….I could go on – and do, in the book – but both technically and conceptually, ‘Recommendation Engines’ are the antithesis of ’narrow.’ Their point and purpose covers the entire sweep of human desire and decision.

A quote in your book is as follows: “Recommenders aren’t just about what we might buy, they’re about who we might want to become”.  How could this be abused by enterprises or bad actors?

There’s no question or doubt that recommendation can be abused. The ‘classic’ classic question – Cui bono? – ‘Who benefits?’ – applies. Are the recommendations truly intended to benefit the recipient or the entity/enterprise making the recommendation? Just as its easy for a colleague, acquaintance or ‘friend’ who knows you to offer up advice that really isn’t in your best interest, it’s a digital snap for ‘data driven’ recommenders to suggest you buy something that increases ’their’ profit at the expense of ‘your’ utility or satisfaction. On one level, I am very concerned about the potential – and reality – of abuse. On the other, I think most people catch on pretty quickly to when they’re being exploited or manipulated by people or technology. Fool me once, shame on you; fool me twice or thrice, shame on me. Recommendation is one of those special domains where it’s smart to be ethical and ethical to be smart. 

Are echo chambers where users are just fed what they want to see regardless of accuracy a societal issue?

Eli Pariser coined the excellent phrase ’the filter bubble’ to describe this phenomenon and pathology. I largely agree with his perspective. In truth, I think it now fair to say that ‘confirmation bias’ – not sex – is what really drives most adult human behavior. Most people are looking for agreement most of the time. Recommenders have to navigate a careful course between novelty, diversity relevance and serendipity because – while too much confirmation is boring and redundant – too much novelty and challenge can annoy and offend. So, yes, the quest for confirmation is both a personal and social issue. That said, recommenders offer a relatively unobnoxious way to bring alternative perspectives and options to people’s attention., However, I do, indeed, wonder whether regulation and legal review will increasingly define the recommendation future.

Filter bubbles currently limit exposure to conflicting, contradicting, and or challenging/viewpoints. Should there be some type of regulation that discourages this type of over-filtering?

I prefer light-touch to heavy-handed regulatory oversight. Most platforms I see do a pretty poor job of labelling ‘fake news’ or establishing quality control. I’d like to see more innovative mechanisms explored: swipe left for a contrarian take; embed links that elaborate on stories or videos in ways that deepen understanding or decontextualize the ‘bias’ that’s being confirmed. But let’s be clear: choice architectures that ‘discourage’ or create ‘frictions’ require different data and design sensibilities than those that ‘forbid’ or ‘censor’ or ‘prevent.’ I think this a very hard problem for people and machines alike. What makes it particularly hard is that human beings – in fact – are less predictable than a lot of psychologists and social scientists believe. There are a lot of competing ‘theories of the mind’ and ‘agency’ these days. The more personalized recommendations and recommenders become, the more challenging and anachronistic ‘one size fits all’ approaches become. It’s one of the many reasons this domain interests me so.

Should end users and society demand explainability as to why specific recommendations are made?

Yes, yes and yes. Not just ‘explainability’ but ‘visibility,’ ’transparency’ and ‘interpretability,’ too. People should have the right to see and understand the technologies being used to influence them. They should be able to appreciate the algorithms used to nudge and persuade them. Think of this as the algorithmic counterpart to ‘informed consent’ in medicine. Patients have the right to get- and doctors have the duty to provide – the reasons and rationales for choosing ’this’ course of action to ’that’ one. Indeed, I argue that ‘informed consent’ – and its future – in medicine and health care offers a good template for the future of ‘informed consent’ for recommendation engines. 

Do you believe it is possible to “hack” the human brain using Recommender Engines?

The brain or the mind? Not kidding. Are we materially – electrically and chemically – hacking neurons and lobes? Or are we using less invasive sensory stimuli to evoke predictable behaviors? Bluntly, I believe some brains – and some minds – are hackable some of the time. But do I believe people are destined to become ‘meat puppets’ who dance to recommendation’s tunes? I do not. Look, some people do become addicts. Some people do lose autonomy and self control. And, yes, some people do want to exploit others. But the preponderance of evidence doesn’t make me worry about the ‘weaponization of recommendation.’ I’m more worried about the abuse of trust.

A quote in a research paper by Jason L. Harman and Jason L. Harman states the following: “The trust that humans place on recommendations is key to the success of recommender systems”. Do you believe that social media has betrayed that trust?

I believe in that quote. I believe that trust is, indeed, key. I believe that smart and ethical people truly understand and appreciate the importance of trust. With apologies to Churchill’s comment on courage, trust is the virtue that enables healthy human connection and growth. That said, I’m comfortable arguing that most social media platforms – yes, Twitter and Facebook, I’m looking at you! – aren’t built around or based on trust. They’re based on facilitating and scaling self-expression. The ability to express one’s self at scale has literally nothing to do with creating or building trust. There was nothing to betray. With recommendation, there is.

You state your belief that the future of Recommender Engines will feature the best recommendations to enhance one’s mind. In your opinion are any Recommendation Engines currently working on such a system?

Not yet. I see that as the next trillion dollar market. I think Amazon and Google and Alibaba and Tencent want to get there. But, who knows, there may be an entrepreneurial innovator who surprises us all: maybe a Spotify incorporating mindfulness and just-in-time whispered ‘advice’ may be the mind-enhancing breakthrough.

How would you summarize how Recommendation Engines enables users to better understand themselves?

Recommendations are about good choices…. sometimes, even great choices. What are the choices you embrace? What are the choices you ignore? What are the choices you reject?  Having the courage to ask – and answer – those questions gives you remarkable insight into who you are and who you might want to become. We are the choices we make; whatever influences those choices has remarkable impact and influence on us.

Is there anything else that you would like to share about your book?

Yes – in the first and final analysis, my book is about the future of advice and the future of who you ‘really’ want to become. It’s about the future of the self – your ’self.’ I think that’s both an exciting and important subject, don’t you?

Thank you for taking the time to share your views.

To our readers I highly recommend this book, it is currently available on Amazon in Kindle or paperback. You can also view more ordering options on the MIT Press page.

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Healthcare

Updesh Dosanjh, Practice Leader, Technology Solutions, IQVIA – Interview Series

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Updesh Dosanjh, Practice Leader of Technology Solutions at IQVIA, a world leader in using data, technology, advanced analytics and expertise to help customers drive healthcare – and human health – forward.

What is it that drew you initially to life sciences?

I’ve worked in multiple industries over the last 30 years, including the life sciences industry in the start of my career. When I chose to come back to the life sciences industry 15 years ago, it was to achieve three ambitions: work in an industry that contributed to the well-being of people; work in an area of industry that could be significantly helped by technology; and to work in an industry that gave me the chance to work with nice people.  Working with a pharmacovigilance team in life sciences has helped me to meet all three of these goals.

Can you discuss what human data science is and its importance to IQVIA?

The volume of human health data is growing rapidly—by more than 878 percent since 2016. Increasingly, advanced analytics are needed to bring to light needed insights. Data science and technology are progressing rapidly, however, there continue to be challenges with the collection and analysis of structured and unstructured data, especially when coming from disparate and siloed data sources.

The emerging discipline of human data science integrates the study of human science with breakthroughs in data technology to tap into the potential value big data can provide in advancing the understanding of human health. In essence, the human data scientist serves as a translator between the world of the clinician and the world of the data specialist. This new paradigm is helping to tackle the challenges facing 21st-century health care.

IQVIA is uniquely positioned to collect, protect, classify and study the data that helps us answer questions about human health. As a leader in human data science, IQVIA has a deep level of life sciences expertise as well as sophisticated analytical capabilities to glean insights from a plethora of data points that can help life science customers bring new medications to market faster and drive toward better health outcomes. By understanding today’s challenges and being creative about how new innovations can accelerate new answers, IQVIA has leaned into the concept of human data science—transforming the way the life sciences industry finds patients, diagnoses illness, and treats conditions.

How can AI best assist drug researchers in narrowing down which specific drugs deserve more industry resources?

Bringing new medications to market is incredibly costly and time-consuming—on average, it takes about 10 years and costs $2.6 billion to do so. When drug developers explore a molecule’s potential to treat or prevent a disease, they analyze any available data relevant to that molecule, which requires significant time and resources. Furthermore, once a drug is introduced and brought to market, companies are responsible for pharmacovigilance in which they need to leverage technology to monitor adverse events (AEs)—any undesirable experiences associated with the use of a given medication—thus helping to ensure patient safety.

Artificial intelligence (AI) tools can help life sciences organizations automate manual data processing tasks to look for and track patterns within data. Rather than having to manually sift through hundreds or thousands of data points to uncover the most relevant insights pertaining to a particular treatment, AI can help life sciences teams effectively uncover the most important information and bring it to the forefront for further exploration and actionable insights. This ensures more time and resources from life science teams are reserved for strategic analysis and decision-making rather than for data reporting.

You recently wrote an article detailing how biopharmaceutical companies that use natural language processing will have a competitive edge. Why do you believe this is so important?

Life sciences companies are under more pressure than ever to innovate, as they strive to advance global health and stay competitive in a highly saturated marketplace. Natural language processing (NLP) is currently being leveraged by life science companies to help mine and “read” unstructured, text-based documents. However, there is still significant untapped potential for leveraging NLP in pharmacovigilance to further protect patient safety, as well as assure regulatory compliance. NLP has the potential to meet evolving compliance requirements, understand new data sources, and elevate new opportunities to drive innovation. It does so by combining and comparing AEs from decades of statistical legacy data and new incoming patient data–which can be processed in real-time—giving an unprecedented amount of visibility and clarity around information being mined from critical data sources.

Pharmacovigilance (the detection, collection, assessment, monitoring, and prevention of adverse effects with pharmaceutical products) is increasingly reliant on AI. Can you discuss some of the efforts being applied by IQVIA towards this?

As mentioned, one of the primary roles of pharmacovigilance (PV) departments is collecting and analyzing information on AEs. Today, approximately 80 percent of healthcare data resides in unstructured formats, like emails and paper documents, and AEs need to be aggregated and correlated from disparate and expansive data sources, including social media, online communities and other digital formats. What is more, language is subjective, and definitions are fluid. Although two patients taking the same medication may describe similar AE reactions, each patient may experience, measure, and describe pain or discomfort levels on a dynamic scale based on various factors. PV and safety professionals working at life sciences organizations that still rely on manual data reporting and processing need to review these extensive, varied, and complex data sets via inefficient processes. This not only slows down clinical trials but also potentially delays the introduction of new drugs to the marketplace, preventing patients from getting access to potentially life-saving medications.

The life sciences industry is highly data-driven, and there is no better ally for data analysis and pattern detection than AI.  These tools are especially useful in processing and extrapolating large, complex PV data sets to help automate manual workloads and make the best use of the human assets on safety teams. Indeed, the adoption of AI and NLP tools within the life sciences industry is making it possible to take these large, unstructured data sets and turn them into actionable insights at unprecedented speed. Here are a few of the ways AI can improve operational efficiency for PV teams, which IQVIA actively delivers to its customers today:

  1. Speed literature searches for relevant information
  2. Scan social media across the globe to pinpoint AEs
  3. Listen and absorb audio calls (e.g. into a call center) for mentions of a company or drug
  4. Translate large amounts of information from one language into another
  5. Transform scanned documents on AEs into actionable information
  6. Read and interpret case narratives with minimal human guidance
  7. Determine whether any patterns in adverse reaction data are providing new, previously unrealized information that could improve patient safety
  8. Automate case follow-ups to verify information and capture any missing data

Is there anything else you would like to share about IQVIA?

IQVIA leverages its large data sets, advanced technology and deep domain expertise to provide the critical differentiator in providing AI tools that are specifically built and trained for the life sciences industry. This unique combination of attributes is what has contributed to the successful implementation of IQVIA technology across a wide array of industry players. This supports integrated global compliance efforts for the industry as well as improving patient safety.

Thank you for the great interview, readers who wish to learn more should visit IQVIA.

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