AI Is Fast Infiltrating Your Life: 5 Indian Startups Show How

[Note: This article is part of The Junction Series. We will be covering the DeepTech sector in detail at The Junction 2017 in Jaipur. Learn more about The Junction here!]

Ever get the nagging feeling that artificial intelligence (AI) has been on the cusp of cutting edge technology for years and years now? Well, to be frank, it has. The term was first coined way back in 1956 at the Dartmouth Conferences and had a simple definition – AI is human intelligence exhibited by machines.

Since then, a variety of computers and programmes have been heralded to carry the baton of AI forward as at those times. However, at every such moment AI carried the same definition but was perceived differently at large – as something more than the culmination of its past produce.

The reason for this being that the definition of artificial intelligence is too simple. Unclear interpretation has always given birth to misuse, and hence, the term AI too has faced plight – especially at the hands of startups today.

Today’s democratised understanding of AI is described by the term – ‘Narrow AI,’ identifying with technologies that perform certain tasks as well as, if not, better than humans. We are today at the stage of technological innovation that stretches from machine learning (linear algorithms used to parse simple data, learn from it, and then make predictions based on prerequisite goals) to deep learning (combination of multitudes of algorithms at different hierarchies that funnel nonlinear data into predictive outputs).

AI has today started gaining traction in more than its parent IT sector. This is because of the increasing capabilities of technology in identifying and recording quantitatively more and qualitatively better data at various stages of every business.

In this article, we look at a few Indian startups, using this modern iteration of AI to benefit different, established industries.

AI in Diagnostic Healthcare

Sigtuple: Sigtuple builds products with cloud-based solution provision capabilities for automated analysis of medical images and data, to aid in the diagnosis process. The automation of analysis is done with the use of machine learning which improves accuracy and brings down the diagnosis time down to a fraction of what its is today. In the healthcare sector, both these factors are especially critical.

The company uses its machine and deep learning capabilities in six diverse diagnostic use cases – Haematology (blood smear analysis), Ophthalmology (retinal scanning and analysis), Andrology (semen analysis), Urinalysis (urine analysis), Radiology (chest X-ray scanning and analysis), and Rheumatology (antinuclear antibody test). It has a panel of consulting clinicians on board.

Cloud-based artificial intelligence will definitely play a massive role in the disbursal of medical diagnosis, especially in rural areas and in developing companies without sufficient monetary resources to equip themselves with the latest in medical screening technologies.

The business in this field too, have a wide untapped market which could yield rich dividends. There are many different varieties of tests that are yet to be initiated in this space and first movers advantage would be crucial. AIndra Systems and VectorDoc are providing competition  in this field to Sugtuple.

AI In Fashion

Staqu: Staqu utilises deep learning and natural language processing-based algorithms to provide reverse image search solutions. The search engine is designed to retrieve critical visual information like content, colour, shape and texture from clicked or uploaded images.

Patterns, colours, and shapes are identified and matched with Staqu’s library of keywords to provide accurate descriptions in the form of meta tags. These tags can effectively be used to provide visual search results and real-time recommendations. This technology has the potential to boost various ecommerce businesses, especially in the field of fashion.

Cataloguing has historically been a large cost- and time-eating factor for fashion-focussed businesses, and helping automate this process will be a very welcome disruption for the industry. With players like Mad Street Den, Artifacia and Streamoid providing competition to Staqu, the sector is getting heated and will be an interesting watch in the near future.

AI In HR Tech

Talview: Talview has made a name in the HR tech space claiming to have processed over 1 Mn candidates with clients in over 100 countries. The company leverages Neuro-linguistic programming (NLP describes the fundamental dynamics between mind (neuro) and language (linguistic) and how their interplay affects our body and behaviour (programming)), machine learning, video interviews and psycholinguistics (study of the relationships between linguistic behaviour and psychological processes) to help clients build better teams at a faster pace.

The startup claims to have identified and overcome a strong market need for a cognitive solution, which can improve the efficiency of hiring. Talview generates over 25,000 data points during its assessments, which when integrated with applicant tracking systems, human resource management systems and social channels, gives recruiters get a complete picture of every applicant.

HR Tech helps make more accurate hirings at a faster pace and is a service that can be applied to almost every company looking to hire. With its large market, the sector is expected to blossom with early players getting a definite advantage. EdGE Networks, Belong and HackerRank are some of the other Indian players in this field.

AI In Financial Services – Regulation

Signzy: Signzy could not have received a more welcome acknowledgement of competence than RBI’s ‘The Most Innovative Solution’ award in June 2016. It provides a digital onboarding solution for banks, NBFCs and other financial institutions by creating a ‘Digital Trust system’. This is accomplished by providing consumer identification, background checks, forgery detection and contract management systems which enable contracting in a ‘simple, secure and compliant’ manner.

The company uses artificial intelligence in its cloud-based APIs that detect the authenticity of information and digital forgery of documents, which usually go undetected by the human eye.

This helps solve crucial trust issues for all three of its stakeholders – the customers, banks, and regulators. An AI-assisted screening approach reduces chances of human fraud and minimises operational efforts for banks.
The company is also trying its hand at another new wave technology – Blockchain. The protocol helps create a complete digital trail of an individual/company’s identity, background information and online transactions, allowing sharing of information without compromising user privacy.

The first process of transacting in the modern digital financial sector is meeting the regulatory requirements of ID or document verification. Today, this is a tedious, mechanical process and prone to human errors. This human ‘error’ has historically been the cause for many identity theft, money-laundering, and fraud cases.

Incumbent players need to surpass trust and security in offline mechanisms and not merely focus on convenience. Signzy has taken up the reins, but existing players like private banks are expected to enter the playing field – sooner rather than later.

Bonus: AI Without Bounds

We have covered a few startups working in specific industries, using AI to create unprecedented efficiencies and capabilities. In this bonus mention, we include startups that create the building blocks to bring the potential of AI to any and every industry!

Arya.ai: Arya.ai builds ‘tools’ that simplify the complex task of building advanced next generation AI-based solutions for developers and enterprises in their respective fields, without having to reinvent the wheel every single time. With a majority of the company’s funding dedicated to investment in Research and Development, it envisions scaling the expertise of building reliable AI – a skill limited to a few people today.    

The company supports building complex neural nets, plugging in data and training networks – all bundled with the convenience of APIs on cloud. The use cases are aplenty, ranging from language modelling and image analysis to risk prediction and energy optimisation. What’s more, it has open sourced some key tools that will foster innovation without bounds and open up a world of new, hitherto undiscovered use cases.

Fluid AI too has similar offerings and together, such startups are the cause of much excitement and expectation.

It is still early in the day to make a call that artificial intelligence can parallel human intelligence in the future. Humans will be hard to replace in certain applications requiring innovation, intuition, and artistic skill. However, the progress seen through the above five examples definitely suggests AI finding its own happy niche in our modernising world. And isn’t co-existence the name of the game?