Ivy’s Data Science Weekly on AI ML

Zoomcar has been instrumental in driving the digitalization of the self-drive ecosystem in India. Analytics India Magazine got in touch with Markish Arun, the Chief Technology Officer of Zoomcar, to understand its AI and data science roadmap for 2021 and how it addresses the data science talent crunch in the market. Zoomcar launched India’s first vehicle model agnostic Driver Score Tech Stack for the passenger car segment. As per Arun, the AI-Powered algorithm with machine learning capabilities tracks the mechanical specs of the car and the driving style of the customer. In addition to that, it tracks and rates the critical events of driving. Not only this allows the operational units to have complete visibility of the car movement but also takes care of the condition, estimated time of arrival, and critical scenarios for the entire trip. 
Lendingkart couples the available data with a machine-learning algorithm to equip lenders with an advanced system. This system assesses risks and approves loans at a much larger scale and lower costs. SMEs (Micro, Small, and Medium Enterprises) have been a significant contributor to India’s GDP, manufacturing output, and exports. However, despite this huge impact of MSMEs on the Indian Economy, the sector is always crippled by the lack of access to credit. 
The company uses advanced machine learning techniques and an open-source software library, XGBoost, with an explainability feature. The scoring platform, on the other hand, is a platform-as-a-service, hosted on AWS and developed using Python. It uses the Apache Kafka framework for real-time scoring and involves a slew of different services, including A/B distribution, insurance APIs, amount policy configuration, etc. “The average time to score an application is approximately 3.5 secs,” said Singh.
Peter Ward, global head of futures and options electronic execution at JP Morgan, tells Hayley McDowell that buy-side adoption of its reinforcement learning FICC futures algorithms has surged in recent years, accelerated by the market volatility in 2020. Amid the arms race in this space, JP Morgan’s Ward predicts the pace of fixed income futures Algo trading adoption, particularly customized algos, will continue apace in 2021. It remains a significant focus at JP Morgan as different buy-side clients are also now using algorithms to trade futures.
The U.S. spy agencies predict an increasingly leaderless and unstable world in the coming decades. In addition to that, artificial intelligence, digital currencies, and climate change cause instability in reshaping the global arena. The above information is according to the National Intelligence Council.
The coming decades will be characterized by a mismatch between global challenges and “the ability of institutions and systems to respond,” according to “Global Trends 2040: A More Contested World.” The result will be “greater contestation at every level” especially between the U.S. and China. Some of the trends from the report include: 
  • Debt pressures
  • Climate pressure
  • New ways of war
  • Space race
  • Permanent populism
  • AI-powered propaganda
We like to imagine an AI-driven future, but it’s machine learning that will actually help us to progress. : Michael I. Jordan.
He wrote in an article for Harvard Data Science Review, we should be talking about ML and its possibilities to augment. Moreover, this does not replace human cognition. And, to be clear, machines are much better at some things. For instance, people could do low-level pattern-matching but at a significant cost, whereas machines are able to perform such mundane tasks at relatively little cost. Another example is that ML is broadly used for fraud detection in financial services. We could have people poring over billions of transactions, but it makes more sense to point computers at the problem.
Not only we know for a fact that most AI projects fail, but also we know the reason behind it now. In Jordan’s emphasis on ML over AI, there’s perhaps a clue as to why AI projects fail (inflated expectations) and how to make ML projects succeed (tightly define projects to augment, not supplant, human actors). 

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