I recently presented the plenary session at a pharma conference covering how Artificial Intelligence (AI) is transforming pharma sales and marketing, I provided examples Eularis had completed for pharma client projects. Several of the attendees sent me emails afterwards wanting to know more about the specific examples I gave, which were as varied as our client needs. It was interesting to learn how few of these types of applications they were familiar with, and I thought the readers of my white papers would want to know about them, too. I've written about many of these topics before, and I'm including those links at the end of each section in case you are interested in digging deeper into a specific topic. According to Takeda Pharmaceuticals, the average time taken to diagnose a rare disease without technology is 7.6 years and comes after countless tests and physician visits. This creates a high cost to the healthcare system, not to mention much suffering for the patient. And some cases are even worse.

Ranking, recommendation, and retrieval systems are widely used in online platforms and other societal systems, including e-commerce, media-streaming, admissions, gig platforms, and hiring. In the recent past, a large "fair ranking" research literature has been developed around making these systems fair to the individuals, providers, or content that are being ranked. Most of this literature defines fairness for a single instance of retrieval, or as a simple additive notion for multiple instances of retrievals over time. This work provides a critical overview of this literature, detailing the often context-specific concerns that such an approach misses: the gap between high ranking placements and true provider utility, spillovers and compounding effects over time, induced strategic incentives, and the effect of statistical uncertainty. We then provide a path forward for a more holistic and impact-oriented fair ranking research agenda, including methodological lessons from other fields and the role of the broader stakeholder community in overcoming data bottlenecks and designing effective regulatory environments.

Motivated by online recommendation systems, we propose the problem of finding the optimal policy in multitask contextual bandits when a small fraction $\alpha < 1/2$ of tasks (users) are arbitrary and adversarial. The remaining fraction of good users share the same instance of contextual bandits with $S$ contexts and $A$ actions (items). Naturally, whether a user is good or adversarial is not known in advance. The goal is to robustly learn the policy that maximizes rewards for good users with as few user interactions as possible. Without adversarial users, established results in collaborative filtering show that $O(1/\epsilon^2)$ per-user interactions suffice to learn a good policy, precisely because information can be shared across users. This parallelization gain is fundamentally altered by the presence of adversarial users: unless there are super-polynomial number of users, we show a lower bound of $\tilde{\Omega}(\min(S,A) \cdot \alpha^2 / \epsilon^2)$ {\it per-user} interactions to learn an $\epsilon$-optimal policy for the good users. We then show we can achieve an $\tilde{O}(\min(S,A)\cdot \alpha/\epsilon^2)$ upper-bound, by employing efficient robust mean estimators for both uni-variate and high-dimensional random variables. We also show that this can be improved depending on the distributions of contexts.

Amid rapidly changing technology, many people still associate artificial intelligence (AI) with science-fiction dystopias. But in reality, AI has become an integral part of our daily lives. We now rely on search-engine algorithms and digital assistants like Alexa and Siri for almost everything, including ordering a taxi or finding out how many calories there are in a 10-inch Margherita pizza. The potential of this technology goes beyond its household use. AI is making great strides in the business world, and creativity may be its ultimate moonshot.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Polk County Sheriff's Office in Florida on Thursday announced charges against 68 suspected drug dealers as part of an undercover operation using social media and dating apps. The six-month operation dubbed "Swipe Left for Meth" -- a play on Grindr and other dating apps that require users to "swipe" through scores of potential dates in their area -- concluded in the arrests of 60 individuals and securement of eight arrest warrants for individuals still at-large related to drug sales or possession. "We've known for some time that suspects will use the internet and social media to prey upon children online, or to engage in prostitution, but this is something we are seeing more and more of in Polk County -- suspects who are using dating apps to sell illegal narcotics," Polk County Sheriff Grady Judd said in a statement." Suspects are getting more creative, but so are our detectives."

In a building called the Partnerplex on Google's sprawling campus in Mountain View, California, I've been invited to hear a 51-second phone recording of someone making a dinner reservation. Person 2: Hi, um, I'd like to reserve a table for Friday the third. Person 1: OK, hold on one moment. As I listen to what sounds like a man and a woman talking, Google's top executives for Assistant, the search giant's digital helper, watch closely to gauge my reaction. They're showing off the Assistant's new tricks a few days before Google I/O, the company's annual developer conference that starts Tuesday. Turns out this particular trick is pretty wild. That's because Person 2, the one who sounds like a man, isn't a person at all.

A 2019 study conducted by DataChildFutures found that 46% of participating Italian households had AI-powered speakers, while 40% of toys were connected to the internet. More recent research suggests that by 2023 more than 275 million intelligent voice assistants, such as Amazon Echo or Google Home, will be installed in homes worldwide. As younger generations grow up interacting with AI-enabled devices, more consideration should be given to the impact of this technology on children, their rights and wellbeing. AI-powered learning tools and approaches are often regarded as critical drivers of innovation in the education sector. Often recognized for its ability to improve the quality of learning and teaching, AI is being used to monitor students' level of knowledge and learning habits, such as rereading and task prioritization, and ultimately to provide a personalized approach to learning. Knewton is one example of AI-enabled learning software that identifies knowledge gaps and curates education content in line with user needs.

So, let us now preprocess our data! Natural Language Processing techniques are our savior when we have to deal with textual data. Since our data cannot be fed to any machine-learning model unless we clean it, that's where NLP comes to play! Let's clean our text data – Firstly, let us create a new column in our dataframe that will hold all necessary keywords required for the model.

Amid rapidly changing technology, many people still associate artificial intelligence (AI) with science-fiction dystopias. But in reality, AI has become an integral part of our daily lives. We now rely on search-engine algorithms and digital assistants like Alexa and Siri for almost everything, including ordering a taxi or finding out how many calories there are in a 10-inch Margherita pizza. The potential of this technology goes beyond its household use. AI is making great strides in the business world, and creativity may be its ultimate moonshot.

Collaborative filtering is the de facto standard for analyzing users' activities and building recommendation systems for items. In this work we develop Sliced Anti-symmetric Decomposition (SAD), a new model for collaborative filtering based on implicit feedback. In contrast to traditional techniques where a latent representation of users (user vectors) and items (item vectors) are estimated, SAD introduces one additional latent vector to each item, using a novel three-way tensor view of user-item interactions. This new vector extends user-item preferences calculated by standard dot products to general inner products, producing interactions between items when evaluating their relative preferences. SAD reduces to state-of-the-art (SOTA) collaborative filtering models when the vector collapses to one, while in this paper we allow its value to be estimated from data. The proposed SAD model is simple, resulting in an efficient group stochastic gradient descent (SGD) algorithm. We demonstrate the efficiency of SAD in both simulated and real world datasets containing over 1M user-item interactions. By comparing SAD with seven alternative SOTA collaborative filtering models, we show that SAD is able to more consistently estimate personalized preferences.