The Infosys Labs research blog tracks trends in technology with a focus on applied research in Information and Communication Technology (ICT)

December 12, 2018

Resetting Robot's Dream

"Cal is a helper house-Robot owned by Mr. Northrop, an author and technology enthusiast. Mr. Northrop is a prolific writer and sometimes loses track of other activities, he likes the way Cal picks up after him, runs his printer, stacks his disks, and other things. He doesn't need a complicated robot and Cal surely fits in. But Cal is a special robot with a level of intelligence not completely explored and with time Cal develops curiosity and interest in writing. More like being influenced by the author persona of his master. As Mr. Northrop comes to know Cal's interest he decides to upgrade Cal with dictionary, vocabulary, grammar, and other essentials for writing stuff. Cal starts writing, initially he wrote random letters like gibberish. But with more upgrades and advice from Mr. Northrop, Cal got better and better. After few attempts Cal wrote a satire with perfect sense of the ridiculous, Mr. Northrop read the story 2-3 times; a sudden feeling of insecurity came to him, what if Cal writes more stories and continues to improve each time? Mr. Northrop decided to undo all improvements and reset Cal as it was when he bought. "

Above is the summary of science fiction short story written by Isaac Asimov in 1991. He wrote many stories on robotics and often credited with devising Three laws of Robotics, which was adapted into  Hollywood sci-fi action film "I, Robot" starring Will Smith.

The vision on future of robotic automation and questions raised by Asimov on freedom of choice is even more relevant in era growing practice of AI. The core issue, that may have prompted Mr. Northrop to take the reset route, is his inability to appreciate the robots did and the grey area around robots decision making which is incomprehensible. Recently Facebook was experimenting with chatbots which were to negotiate among each other for ownership of virtual items, but after a few rounds the AI programs seemed to be interacting in a language that only they understood; Facebook had to shut down the experiment.

Transparency is a major factor that we need to address for building sustainable AI systems, in above case had Mr. Northrop knew that Cal was only trying to mimic him for extending help rather than being a competition, his action could have been different. Along with that interpretability and explainability of decision taken by AI systems would nullify grey areas, thereby building confidence among user community on trustworthiness of the systems. The factors will be crucial as organizations sail through the transformation journey of industry 4.0 where AI will have significant penetration across industry verticals.

To stay ahead with the AI curve, Organizations must build trust in their AI application. That will also speed up adoption of AI application among the stakeholders within and outside the organizations. For example, there is huge potential for AI in banking sector. In areas like traditional loan approval value chain from application to disbursement, AI can be applied at stages such as validation, due diligence, and approval; but lack of trust & transparency in AI applications hinders the adoption of AI led loan evaluation process. There are many such cases across industries like customer recommended in retail, optimizing the distribution of energy, fraudulent reimbursement in insurance etc.

Moving on to digital era we will be surrounded smart AI systems and would interacting with real life CAL s for day-in day-out. So, it's our imperative to build robust mechanism for explainability as well as trusted and sustainable AI systems.

Continue reading "Resetting Robot's Dream" »

December 7, 2018

Rise of Emotional Intelligence in AI

We typically prefer to be with people who can understand us and are emotionally intelligent. Body language and tone play a significant part in what we think and feel. Emotional intelligence encompasses the ability of people to recognize, understand and control their own emotions as well as recognize, understand and influence others' emotions. EQ has become an important consideration when we talk about AI development. As per Rana el Kaliouby, co founder and CEO of Affectiva, an MIT spinout company that works on emotional recognition technology, "If it's interfacing with a human, it needs social and emotional skills." The addition of EQ to AI will help such systems respond better to more complex human needs leading to creation of better customer experiences and thereby improve customer satisfaction.

Businesses are increasingly benefitting from advances in emotionally intelligent AI as they uncover new opportunities by understanding consumer likes and dislikes along with gauging their affinity towards a brand or product. As per a recent study by Market Research Future (MRFR), the global emotion analytics market is expected to reach USD 25 billion by 2023, growing at a CAGR of 17% between 2017 and 2023. Also, Gartner predicts that by 2022, 10% of our personal devices will include emotional AI capabilities, up from less than 1% in 2018. Using sentiment analysis to understand consumer perception towards a product/brand in the offline world has remained a daunting task. Detecting emotions from facial expressions using AI can be used as a substitute to better understand consumer preferences and how they engage with particular brands.

Traditionally market research companies have relied on using different methods such a surveys, trade interviews to better understand consumer requirements. However, these methods assume a direct correlation between future actions and what the consumers state verbally, which may not always be accurate. In this scenario, behavioral methods are considered more objective and are often deployed to observe a user's reaction while interacting with a product/brand. Manually analyzing video feeds of users interacting with a product/brand can be pretty labor intensive. Facial emotion recognition can be useful in this scenario as they allow market research companies to record facial expressions automatically and derive meaningful insights from them.

Disney has designed an AI-powered algorithm to gain a better understanding of how audiences enjoy its movies, this algorithm can recognize complex facial expressions and also predict how audiences will react for the remaining part of the movie. As per reports, the tests processed a staggering figure of 16 million data points derived from 3,179 viewers.

Earlier this year, Soul Machines partnered with Daimler Financial Services to present "Sarah", a digital human as an interface to Daimler's financial services and mobility ecosystem aiding them to deliver enhanced customer experiences in the areas of car financing, leasing and insurance by utilizing facial gestures and natural voice intonation.

Annette Zimmermann, vice president of research at Gartner claimed in January 2018, "By 2022, your personal device will know more about your emotional state than your own family." Facial analysis, voice pattern analysis and deep learning when used together in conjunction can help decipher human emotions with applications across a broad range of industries such as retail, financial services, medical diagnosis, autonomous cars, fraud detection and recruitment among others.

The shift from data-driven interactions relying heavily on IQ to EQ-guided experiences will also present companies an opportunity to connect with customers on a much more intimate level. However, emotions are immensely personal and companies working in this space should be wary about consumer concerns such as intrusion of personal space and manipulation. Suitable psychological training for people is also required to interpret emotional results from these machines and fix deviations as deemed appropriate.

November 30, 2018

Explainable AI - Introduction and applications

AI systems have essentially remained black boxes, with deep learning models frequently remaining opaque. It has become imperative to build systems which can justify their decisions, very similar to how humans operate. Significant advances in this area will result in the evolution of autonomous systems that are able to learn, make decisions and implement them without the support of any external agents. Explainable AI (XAI) is artificial intelligence that is programmed to describe its purpose, rationale and decision-making process in a way that can be understood by the average person. Powerful algorithms often churn out useful results, without explaining how they arrived at it. Thus, transparency is often compromised while arriving at sophisticated experimental results using AI systems. As AI models become more complex, it is important for these systems to provide verifiable explanations of the decisions they make. Key benefits derived from the implementation of XAI include:

·         Aid in faster and broader deployment of AI

·         Bring convenience and speed to consumers along with building trust

·         Adoption of best practices around the areas of compliance, accountability and ethics

·         Reduce impact of biased algorithms

The figure below illustrates the concept of XAI as demonstrated by Defense Advanced Research Projects Agency (DARPA):

Explainable AI.jpg

                                                                   Source: XAI Concept by DARPA

AI systems have multiple applications across industries. For example, in the financial services domain it will be important for AI systems to be able to explain their decision making in order to be fully embraced and gain trust in the industry. If a loan application process is denied by an automated system powered by AI, bank executives should be able to trace the decision to the specific step where the denial occurred and also provide a reasoning for the AI system's decision at that particular step.

An AI system which is determining the premium charges for car insurance should also be able to provide the rationale behind such a decision based on several factors including age, gender, car type, accident history, address, mileage etc. It should also aid in providing a personalized experience by mentioning what the customer needs to do in order to reduce premium charges, for example drive accident free for the next one year.

An ethical risk is also prevalent in this scenario as bias can unintentionally creep into algorithmic models and thereby result in discriminatory practices. This puts organizations at risk as consumers are likely to switch brands once they understand about these prejudices. For example, certain existing AI algorithms imposed higher charges for Asian Americans opting for SAT tutoring. Facial recognition software is being increasingly used for law enforcement and is also promulgating racial and gender bias. Earlier this year, Joy Buoalamwini from the Massachusetts Institute of Technology showed that gender-recognition AIS from IBM, Microsoft and Chinese company Megvii were able to identify gender from a photograph for white men with an accuracy of 99%. However, this number was staggeringly low at 35% for dark-skinned women. This poses increased risk towards false identification of women and minorities.

Explainable AI will thus help to build models which can identify relevant stakeholders and the information they require about how the model arrives at decisions. This would also identify any form of bias which has crept in and aid data scientists weed them out at an early stage. Eventually as humans and machines work together more effectively, it will be imperative for us to understand the machine logic lying underneath.

Transparency will become an important requirement to keep up with compliance regulations. For example, the General Data Protection Regulation (GDPR) with a focus on right to explanation mandates that users should be able to demand data behind algorithmic decisions made by recommendation engines. This puts the onus on companies to translate complicated reasoning behind AI algorithms to simple and easily interpretable language.

September 30, 2018

Rise of Digital banks!

Our computers have become windows through which we can gaze upon a world that is virtually without horizons or boundaries. 
                                                                                                                                                ~ Joseph B. Wirthlin

Ever complained about standing in queues and having to sign countless banking forms? Ever wondered why you need to walk into a branch of your bank to perform mundane tasks which can be easily done with a tap on your smartphone? If you have, then you are not alone. Welcome to the new world of banking where several startups and even some traditional banking giants are experimenting with all online and fully functional digital banks. These digital banks are industriously working towards addressing many of the consumer pain points in the business as usual banking world.

Digital-only banks are on the rise. And by digital-only banks, I am referring to truly digital banks who do not have any brick and mortar presence. They have an edge over traditional banks in terms of efficiency, speed, ROI and scale. Being fully automated, they are more efficient than their traditional counterparts where still a lot of processing is manual. Digital banks deliver services faster than a brick and mortar business while being efficient at the same time. 

Setting up a digital bank is again more pocket friendly when compared to a regular bank. Also, a digital bank can be scaled up to meet rising consumer demands much more easily. Another major advantage is in terms of readily available data. Digital banks can undertake AI/ML initiatives with much more ease to deliver faster insights and implement changes with reduced time to market. Data cleansing and curation process becomes seamless.

Digital banks are very much powered by technology and are at the forefront of experimenting with all the cutting-edge advances available. AI powered virtual assistants, digital only cash, peer-to-peer lending and payments and blockchain based banking transactions have all been tried out in digital banking arena. Touch ID serves as an efficient security tool which provides a safe way to login to banking accounts.

Digital banks come in all flavors. Some banks offer zero maintenance fees. Few others offer rewards based on your social media likes. Revoult, a digital-only bank, offers global payments and cryptocurrency exchange. Yet another section of banks cater to a very niche segment of consumers. For example, USAA is a member only bank serving US military members and their kin.

Banking is at the cusp of a digital revolution similar to what the retail industry witnessed few years ago. Amazon disrupted brick and mortal retail stores with an all-online market. Likewise, digital banks are all set to disrupt traditional banks. Agreed there are rigid regulations and compliance in place which digital banks will have to adhere to; yet they are flourishing at a super fast pace. Banking as a platform or banking as a service may become the norm in banking sooner than later.


September 26, 2018

Sports and Technology

Technology has made its way into aspect of our life. It has changed the way we work, travel and live. During this apparent transformation sports, an aspect of our lives enjoyed by all has been leveraging technology to enhance their performance and reach out to their loyal fans.

The introduction of television into our lives was a game changer for sports. TV's enables viewers to watch the game from the comfort of their homes and follow their favorite teams no matter where they played. It also enabled fans to access and learn the various sports played around the world. Whether it was sports like soccer or F1, television increased their fan base like no other technology had ever before. New revenue streams were created for the sports teams and associations through the sale of broadcasting rights.

Many sports have also embraced technologies for providing replays, to review umpire decisions and such predict the direction of the ball. The Hawk-Eye system used to predict the direction of the ball has already been embraced by various sports such as tennis and cricket.

But, a new wave of technology is promising to revolutionize the way we enjoy our sports. Startups are designing jerseys which the fan can wear to feel the intensity of the game through haptic feedback which is generated by the adrenaline and excitement of their favorite NFL team. The technology brings the feel of a stadium to fans watching the game from home. Another such application used the NFL is Be the Player, the application allows fans to watch the game from the point of view of their favorite player without having the player wear a camera.

Sports Associations are revolutionizing fan engagement by leveraging social media sites and virtual games. Fans can now get personalized feed of the NFL games enabling fans to select players they want to follow and watch off the field clips of the team in the locker room and post win parties. The NBA is engaging fans on the internet by counting votes through social media, google, etc. to select players for the All-Stars game. They even have a chatbot which can show clips of players or matches based on requests from fans.

Virtual reality is another aspect being embraced by technology. Games are now being telecast for fans to watch through VR in order to provide a more immersive experience for remotely viewing fans.

The emergence of new technology and the demand for higher levels of engagement from fans are forcing sports teams and associations to search and identify new channels of engagement. With the rapid rate of adoption of technology, it won't be long before we will be able to immerse ourselves in the excitement of the game and be closer to our favorite players than ever before.


Artificial Intelligence & Financial Services - The Applications

In my previous blog "Artificial Intelligence & Financial Services - The Business Case" we explored the business case for adoption of AI in the financial industry. In this blog we will look at the technology, its applications and its adoption by the industry.

The most mature use cases are in chatbots in the front office, antifraud and risk and KYC/AML in the middle office, and credit underwriting in the back office.

Front office operations have leveraged chatbots to revolutionize customer relationship management. Other than assisting customers with their transactions, chatbots enable banks to segment customers individually rather than general buckets by collecting data regarding their behavior and habits. Infact, Nina, Swedbank's AI chatbot was deployed to assist customers 2 years ago. Already, Nina has successfully demonstrated its ability to resolve 78% contact resolution and has a customer adoption rate of 30,000 conversations per month. By 2024, 42.82% of the estimated $1.25 billion market for chatbots is expected to be generated by the rising need for enhancing the customer services to retain existing customers and attracting potential customers.

Similarly, AI based anti-fraud and KYC/AML applications have been gaining traction in middle office operations thanks to the superior cognitive capabilities of AI. The digitization of banking products and services has led to an increased susceptibility to fraud. Using AI significantly reduces the time taken to review transaction, including all the factors and relevant data associated with it. Lloyds Banking Group is using AI models that detect when the person logged in is not the customer, but a fraudster or a bot. Similarly, Natwest has been using AI to reduce fraudulent transactions and reported that AI has prevented 7 million Pounds of false payments.

Back office operations too are undergoing a change. AI is used for applications such as credit and risk underwriting in the back office by creating a more complete and unbiased assessment of an applicant's credit worthiness. AI based underwriting provides a more comprehensive view of the assessee' s credit worthiness. Startup, Lenddo has already enabled their partners to assess 5 million applicants through the 12,000 variables collected from alternative sources.

AI has also been widely adopted by hedge funds for algorithmic trading, AI collects data from several sources to create a more accurate prediction. They are also being used by banks to discover investment opportunities by scouring the markets. CircleUp, a venture capital firm has created Classifier, a machine learning crowdfunding platform to determine which companies to fund. The Classifier has the capability to review 500 opportunities per month with a team of less than 10 analysts vs the 500 evaluations per year done by the average private equity firm.

While chatbots, anti-fraud, risk management, KYC/AML, credit underwriting and asset management are being adopted by the financial industry, the other applications are generating a lot of interest and it won't be long before they too go mainstream. With AI estimated to add more than 1 trillion in value to the financial industry it won't be long before robots cater to our financial needs while algorithms manage our daily finances.


Artificial Intelligence & Financial Services - The Business Case

Artificial Intelligence & Financial Services - The Business Case

The new wave of innovation and technology commonly referred to as "fintech" is reshaping the financial services industry and forcing the critical financial intermediaries to adopt emerging technologies. AI has been the focus of financial institutions due to the opportunities arising from the rise in volume of data, speed of access to it and the emergence of new and advanced algorithms able to analyse data in a more intelligent. Industries are leveraging the technology to gain a competitive advantage against their peers by improving speed, cost efficiency and accuracy of processes and meeting rising customer expectations.

The highest adoption rates of AI in financial services companies are in IT with 63.5%, finance and accounting with 40.4%, marketing with 31.4% and customer services with 30.8%. Challenges with the adoption of AI in the financial service industry has been the auditability and traceability of the applications. Financial institutions have to comply with regulations requiring them to explain their decisions to customers and report the same to regulators.


Cost savings is expected to be the primary driver of AI in the financial industry, analysts estimate that AI will save the banking Industry $1 trillion in savings by 2035 with most of the savings coming from the front office. Although the changes are predicted to be gradual until 2025, the adoption rate is expected to accelerate until 2030.


Reduction in the scale of retail branch networks and other distribution staff will generate most of the savings in the front office with $199 billion. Chatbots are expected to take over and handle upto 85% of the world's customer interaction. Chatbots are already being leveraged to help customers manage their personal finances, provide investment advice and suggest the best product for the customer while enabling the customer to perform simple transactions effortlessly.

The application of AI for compliance, KYC/AML and data processing is forecasted to save $217 billion in the middle office. One of the mature applications, KYC/AML uses pattern detection d unstructured text analysis to identify potential fraudulent activity in real time while identifying complex linkages between entities.

Back office operations are also expected to generate $200 billion in savings of which $31 billion will be generated through the application of AI for underwriting and collection systems. AI enables underwriters to collect data from alternative sources such as social media and geolocation data enabling to assess candidates with limited credit history and speed up the entire process.

Applications of AI range from customer service through AI assistants to process automation tools for eliminating time intensive work. Irrespective of whether its intelligent automation for repetitive manual tasks, the enhanced judgement and improved interactions provided by AI is the future of the industry and will drive enterprise growth and profitability in the years to come.

In my next blog "Artificial Intelligence & Financial Services - The Applications", we will dive deeper into the application of AI in financial services.


Deloitte: Ai and risk management - Innovation with Confidence

Journey towards Adaptive Care

Historically healthcare has been intermittent and reactive in nature. Even in today's world of digital, mobile, and technological breakthroughs (both medical sciences and ICT) when it comes to personal care people tends to follow a reactive to disease approach. That might be suitable for a sick care scenario but journey towards continuous and proactive healthcare will require a more connected environment, personalization, better patient experience, home care, and predictive medications. Rather than relying on intermittent data for diagnostics decision, patient should be at the center of care system which will give a complete view of the patient's biological, physical, mental status.

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September 21, 2018

V2X Next wave of transportation

Advancement in the societal and new market trends leading to revolution in personal mobility and vehicular transport system. Societal trends such as rapid growth of urbanization putting pressure on current transportation setup, which is growing less compared to the demand, tough emission and energy related regulation are also impacting transportation systems. Apart from this, market trends as advancement of automated driving, real-time and open data accessibility, enabling more effective use of transport assets and also affecting the current transportation systems.

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Platformization, the new frontier for IT services

It was another busy day at office for Mr. Nayak, at 6 PM it's time to start for home. But, he is not going home today, how can he forgot his anniversary; he specifically set a reminder for it, after the goof up of last year. Mr. Nayak searches for wine & dine restaurants in his smartphone, the app automatically suggests him options for buying flowers and chocolates around the searched location.

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