There have been fast-paced advancements in Artificial Intelligence and related technologies, which has led to AI applications being used in everyday life. Automated customer support systems, chatbots, and personalized shopping experience with product recommendations are common examples. Demand for artificial intelligence and machine learning specialists in the country is expected to see a 60% increase this year due to the increasing adoption of automation, as per a report by KellyOCG India. Artificial Intelligence is influencing people and businesses on a massive scale and has become indispensable. The Artificial Intelligence Industry in India is currently estimated to be US$ 230 million (annual) in terms of revenue, up from US$180 million a year ago.

Personalizing drug prescriptions in cancer care based on genomic information requires associating genomic markers with treatment effects. This is an unsolved challenge requiring genomic patient data in yet unavailable volumes as well as appropriate quantitative methods. We attempt to solve this challenge for an experimental proxy for which sufficient data is available: 42 drugs tested on 1018 cancer cell lines. Our goal is to develop a method to identify the drug that is most promising based on a cell line's genomic information. For this, we need to identify for each drug the machine learning method, choice of hyperparameters and genomic features for optimal predictive performance. We extensively compare combinations of gene sets (both curated and random), genetic features, and machine learning algorithms for all 42 drugs. For each drug, the best performing combination (considering only the curated gene sets) is selected. We use these top model parameters for each drug to build and demonstrate a Drug Recommendation System (Dr.S). Insights resulting from this analysis are formulated as best practices for developing drug recommendation systems. The complete software system, called the Cell Line Analyzer, is written in Python and available on github.

Researchers discovered the dating app Plenty of Fish was leaking information that users had set to private on their profiles. User's names and zip codes were displayed in the app's API, allowing malicious actors to locate a user's exact location. Although the data was scrambled, experts were able to reveal the information using freely available tools designed to analyze network traffic, as first reported by TechCrunch. The discovery was made by The App Analyst, an expert in digital apps, who found that sensitive data was visible via Plenty of Fish's API on October 20th. A fix was developed and tested on November 5th and on December 18th, it confirmed the sensitive data was no longer present in its API.

There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives. AI assists in every area of our lives, whether we're trying to read our emails, get driving directions, get music or movie recommendations. In this article, I'll show you examples how artificial intelligence is used in day-to-day activities such as: Artificial intelligence makes it easier for users to locate and communicate with friends and business associates. From tweet recommendations to fighting inappropriate or racist content and enhancing the user experience, Twitter has begun to use artificial intelligence behind the scenes to enhance their product. They process lots of data through deep neural networks to learn over time what users preferences are.

WITH record fines dished out over tech firms' use of personal data, and their public images becoming increasingly tarnished, this was the year the world started to turn against its tech giants. At the beginning of 2019, France's National Commission on Informatics and Liberty hit Google with a €50 million fine for lack of valid consent and transparency around personalised ads. In October, Facebook agreed to pay a fine of £500,000 to the UK Information Commissioner's Office for failing to protect users' personal information relating to the Cambridge Analytica scandal. Although the firm didn't admit fault over data misuse, this is the largest fine that could be issued. Amazon, Apple and Facebook all faced criticism this year over revelations that staff and contractors had listened to audio recordings of people speaking to virtual assistants Alexa and Siri, and voice chats recorded on Facebook Messenger.

In 2011, Apple unveiled its first iPhone with artificial intelligence, a personal assistant named Siri that could answer questions and help keep track of our daily lives. The AI revolution had begun, and it gave way to higher resolution cameras on phones, such as the then-new iPhone 4S, microphones and cameras in the home, everything from connected speakers, security devices, computers and even showers and sinks. By the end of the decade, we were carrying and or living with devices that are capable of tracking our every movement. Counties and states are selling our personal information to data brokers to resell it back to us, in the form of "people search engines." Facebook and Google have refined their tracking skills, in the pursuit of selling targeted advertising to marketers, that many people believe they are listening to us at all times. They are that good at serving up ads based on our interests, whether we want it or not.

Autoencoders are a common building block of Deep Learning architectures, where they are mainly used for representation learning. They have also been successfully used in Collaborative Filtering (CF) recommender systems to predict missing ratings. Unfortunately, like all black box machine learning models, they are unable to explain their outputs. Hence, while predictions from an Autoencoder-based recommender system might be accurate, it might not be clear to the user why a recommendation was generated. In this work, we design an explainable recommendation system using an Autoencoder model whose predictions can be explained using the neighborhood based explanation style. Our preliminary work can be considered to be the first step towards an explainable deep learning architecture based on Autoencoders.

Though used extensively, the concept and process of machine learning (ML) personalization have generally received little attention from academics, practitioners, and the general public. We describe the ML approach as relying on the metaphor of the person as a feature vector and contrast this with humanistic views of the person. In light of the recent calls by the IEEE to consider the effects of ML on human well-being, we ask whether ML personalization can be reconciled with these humanistic views of the person, which highlight the importance of moral and social identity. As human behavior increasingly becomes digitized, analyzed, and predicted, to what extent do our subsequent decisions about what to choose, buy, or do, made both by us and others, reflect who we are as persons? This paper first explicates the term personalization by considering ML personalization and highlights its relation to humanistic conceptions of the person, then proposes several dimensions for evaluating the degree of personalization of ML personalized scores. By doing so, we hope to contribute to current debate on the issues of algorithmic bias, transparency, and fairness in machine learning.

Machine learning, a subset of artificial intelligence (AI), enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information. There are those who still associate artificial intelligence (AI) and machine learning (ML) with science fiction novels and movies like the Matrix. In reality, machine-learning is already with us, seeping into our everyday lives without much fanfare.

Deepak: AI applications in the Indian pharma industry has definitely not caught up as much as it has in some other industries like e-commerce or Financial services. With AI there is always a need for scale (as scale allows learning to happen faster) and most of the attempts have hence been consumer or patient facing. In the developed world we do hear about large scale solutions in imaging and diagnosis, in clinical trials and in pipeline success measurement. Specifically, within Abbott we have identified a few focus areas namely forecasting, decision tree applications like attrition prediction, robotic process automation in internal data management processes and in delivering superlative user experience for the salesforce through Maya, our virtual assistant for the sales reps and in recommendation engines in KnowledgeGenie. Deepak: AI has a huge role to play in managed services and backend support.