Are you scared yet, human? Artificial intelligence (AI) has proliferated with transformative effects in recent years, in sectors from autonomous vehicles to personalised shopping. But the latest deployment of AI to generate content such as text, images or audio has caused quite a stir. ChatGPT, a particularly superior language model, even passed the US medical speciality exam. That's not to say there haven't been some bloopers.

Zain Jaffer is the founder and president of Zain Ventures, a family office that invests in real estate and proptech. Anyone who has tried out ChatGPT in recent weeks will be amazed at the state of artificial intelligence (AI) in the world today. Recently, Microsoft announced that they would be investing $10 billion in OpenAI, the startup that created ChatGPT. Clearly, AI is the buzzword of the times. People have been predicting that AI could replace many of the mundane and repetitive tasks that we humans do.

Artificial Intelligence, the term which first originated in the 1950s has now emerged as a prominent buzzword all over the world. More than 15% of companies are using AI and it is proving to be one of the most powerful and game-changing technology advancements of all time. From Siri to Sophia, the technology has people noticing it and wondering how this will impact their future. Presently, Artificial Intelligence is seen everywhere. Major industries like healthcare, education, manufacturing, and banking are investing in AI for their digital transformation. Cybersecurity, being the major concern of the digital world, is still uncertain about the impact AI will have on it. With the fast-growing cyber attacks and attackers, cybercrime is growing to become a  massively profitable business which is one of the largest threats to every firm in the world. For this very reason, many companies are implementing Artificial Intelligence techniques which automatically detect threats and fight them without human involvement. How AI Is Enhancing Cybersecurity Artificial Intelligence is improving cybersecurity by automating complicated methods which detect attacks and react to security breaches. This leads to improvement in monitoring incidents leading to faster detection of threats and its consequent responses. These two aspects are quite essential as they minimize the damages caused. Various Machine Learning algorithms are adapted for this process depending on the data obtained. In the field of cybersecurity, these algorithms can identify exceptions and predict threats with greater speed and accuracy.

Artificial intelligence (AI) is poised to significantly disrupt the global financial services system--creating both new opportunities and increased vulnerabilities--according to a recent August 2018 report, The New Physics of Financial Services: How artificial intelligence is transforming the financial ecosystem, published by the World Economic Forum (WEF) in collaboration with Deloitte. WEF predicts the eventual fall and elimination of mid-sized financial service companies as AI will benefit scale-based players that can compete on cost, and present agile players with new market opportunities in underserved niche segments. According to DBR Research, only 7 percent of banks with assets between $1 billion and $10 billion dollars have deployed an AI solution, a sharp contrast to 48 percent of banks with greater than $50 billion in assets. WEF predicts that early adopters of AI will transform their back-office operations from a cost center to a revenue-generating external service as a cloud-based "software as a service" (SaaS) provider. For example, U.S.-based Blackrock, one of the world's largest asset managers, has developed a hosted proprietary risk-analytics and portfolio management platform called Aladdin that utilizes AI machine learning as a differentiator.

Most of my other projects are concerned with large-scale virtual screening applications: In collaboration with labs that do experimental biology, we develop and apply methods to predict candidate molecules that either inhibit (or activate, depending on the project) an individual protein, in absence or presence of a protein crystal structure. The interesting part about it is the interplay between predictions and feedback: I get to make predictions and (at some point), I get the experimental results to see whether I was right or wrong and to analyze why certain predictions worked better than others. Another exciting challenge in such projects is that one has to find ways to make this all computationally feasible -- if you have 15 million molecules, selecting 100 candidate molecules for experimental testing is a bit like searching for the needle in the haystack. Usually, it comes down to formulating specific hypotheses upfront as "filtering" steps since a brute-force docking, which computationally not feasible (since we also have time constraints). My projects require a certain amount of creativity and technical skills to put the ideas into action, but eventually, the approach (the hypotheses) also have to make sense to our collaborators (and the funding agencies).

I am not working in the health-care field, but I met several people who are working at the intersection between machine learning and health-care. Mias Lab) focusses on collecting and integrating omics data from various online databases and resources to predict risk factors for certain diseases. And Samantha Kleinberg, Author of Why, is doing remarkable research, applying and developing various statistical modeling techniques related to health care (Samantha Kleinberg). Looking at the biomedical literature, I think that the classic approach for characterizing the function of a particular protein or gene is to look at it in isolation (knocking it out or overexpressing) to link it to a certain phenotype. This bottom-up approach is certainly necessary to identify the key players related to health.