Emerging tech trends firms just can’t ignore

The advance of AI

According to Convergence Catalyst’s June 2017 report on artificial intelligence (AI), it took between 19 and 40 million pictures of cats for an average unsupervised (unaided) deep learning algorithm to recognize a picture of a cat. A follow-up research on the same topic earlier in February this year pegged the number down to less than one million—thanks to the significant advancements in low-data techniques, clustering algorithms and subject vector machines in the AI/ML (machine learning) space.

Moreover, computing power used in the largest AI training runs has been doubling every 3.5 months. Since 2012, this metric has grown by more than 300,000x. Furthermore, generative adversarial networks (GANs), the technique of pitting two neural networks against each other to create a new piece of content is fast coming of age and is being adopted. This technique elevates machines from being just learning entities to those that start creating and eventually evolve into beings with imagination.

Reinforcement learning, a third type of ML technique (apart from supervised and unsupervised learning) that uses evaluation and trial-and-error as learning methods (as opposed to instructions) is rapidly advancing and getting implemented in robotics.

A ‘speedier’ blockchain

Third-generation blockchain technologies (such as Neo, Stellar and Hashgraph) built on DAG (directed acyclic graph) technology, which remove the miner from the equation, have been gaining popularity and replacing Ethereum as a blockchain platform of choice since the beginning of 2018. Theoretically, these technologies can handle hundreds of thousands of transactions per second as compared to six-seven transactions per second by the first-generation blockchain; we have witnessed certain DAG-based platforms perform over 10,000 transactions per second in real-world scenarios.

AI with Internet of Things (IoT)

In the past couple of years, this trend has witnessed expeditious adoption in various sectors, primarily in smart manufacturing, Industry 4.0, smart grids, oil rigs and refineries, wind farms, and retail and logistics. While these industries have been experimenting with sensor-enabled automation for quite some time, the focus with IoT is now on AI/ML and security.

Small firms use AI, too

Large tech companies, as usual, are playing their part in advancing the emerging technologies: cases in point being Apple’s recently launched and faster Core ML 2; Amazon’s continuous improvement of its virtual assistant, Alexa; and Google’s announcement of Duplex—the technology that carries out natural language conversations over the phone to complete real-world tasks.

There are also a number of small, young companies that are innovating in and disrupting this space. Among them is a one-member firm that used the neural networks research in cancer genomics to develop an underwriting platform for insurance and financial services industries; a Bengaluru-based computer vision start-up that cracked one-shot imitation learning technique to build a video analytics product for retail; and an Indian online fashion marketplace working on GANs to design apparel.

What should firms do?

Convergence Catalyst has witnessed growth in AI/ML and blockchain projects across travel, localization, fund management, media, cybersecurity, real estate, higher education and sports domains. However, when large companies with established business verticals adopt a wait-and-watch policy in reacting to or adopting new technologies, they are not entirely at fault. Their top executives are sceptical, as they have lived through the buzzword-infused hype cycles in the past. This time around, though, the developments in emerging technologies have garnered critical mass and speed. Hence, companies can no longer ignore this trend.

Even a digital master like Facebook is evolving from its core tenet of “connecting everyone on earth" into a media company. This is due to its culture of “embracing change" and being constantly on the lookout for “Facebook killers" and pre-empting them. To stay ahead of the race, firms must emulate such models—of course, without compromising privacy laws.

Jayanth Kolla is a partner with Convergence Catalyst.