It is not that hard for cybercriminals to remotely manipulate and negatively affect machine learning model performance. Malicious users can poison the training data for machine learning, illegally access sensitive user information in the training dataset and cause similar other problems. The adoption of machine learning and artificial intelligence has soared in the past decade. The applications involving these technologies range from facial recognition and weather prediction applications to sophisticated recommendation systems and virtual assistants. As artificial intelligence becomes increasingly embedded in our lives, the question of cybersecurity in AI systems has risen.

Yesterday, California-based AI firm Adept announced Action Transformer (ACT-1), an AI model that can perform actions in software like a human assistant when given high-level written or verbal commands. It can reportedly operate web apps and perform intelligent searches on websites while clicking, scrolling, and typing in the right fields as if it were a person using the computer. In a demo video tweeted by Adept, the company shows someone typing, "Find me a house in Houston that works for a family of 4. My budget is 600K" into a text entry box. Upon submitting the task, ACT-1 automatically browses Redfin.com in a web browser, clicking the proper regions of the website, typing a search entry, and changing the search parameters until a matching house appears on the screen. It's called Action Transformer (ACT-1) and we taught it to use a bunch of software tools.

Conventionally, salespeople are responsible for juggling tasks like following a playbook, capturing responses, building rapport and updating a customer relationship management (CRM) system during sales calls. As these tend to be repetitive and time-consuming, tedium can quickly set in. The average salesperson spends more than five hours a week updating CRM records, according to a Dooly survey. In search of a solution, sales tech entrepreneur Eldad Postan-Koren and cybersecurity practitioner Bar Haleva co-created Winn.AI, an AI-powered assistant designed to help sales teams automatically track, capture and update CRM entries. Winn.AI monitors sales calls and records key data, in theory reducing the need for salespeople to note-take themselves.

Winn.AI handles the busywork of sales, so salespeople can focus on selling. Capture meeting notes & customer data in real-time and update your CRM in a click.

Years ago, artificial intelligence (AI) was only exclusive to scientific research. Nowadays, you can also find AI in the eCommerce industry, as it has become an invaluable tool to many companies in the online space. Numerous businesses use AI to reduce operating expenses, improve analytical insights, and stay ahead of competitors. There's no doubt that AI provides many new opportunities for eCommerce, which is why more and more companies adopt this technology. The growth of AI is at a rapid pace.

Thank you for calling …" A sad sort of human-to-computer stalemate has played out over countless fruitless interactions. Companies adopted IVR (interactive voice response) systems over the last decades, possibly in an attempt to reduce the cost of hiring and training costs for human customer service reps that have high turnover rates. Or, perhaps some forward-thinking executives thought a robot-voiced CSR would make a company appear more'advanced' in comparison to its competitors. Whatever the reason, our earliest conversations with IVR menus and chatbots left most of us humans feeling let down, like we weren't having a conversation at all. Despite the fact that voice recognition and computer speech have improved dramatically in speed and sophistication, it's hard for some of us to shake that feeling that nobody is on the other end of the line to help.

Recommender systems, which offer personalized suggestions to users, power many of today's social media, e-commerce and entertainment. However, these systems have been known to intellectually isolate users from a variety of perspectives, or cause filter bubbles. In our work, we characterize and mitigate this filter bubble effect. We do so by classifying various datapoints based on their user-item interaction history and calculating the influences of the classified categories on each other using the well known TracIn method. Finally, we mitigate this filter bubble effect without compromising accuracy by carefully retraining our recommender system.

Collaborative Filtering (CF) is a widely used technique which allows to leverage past users' preferences data to identify behavioural patterns and exploit them to predict custom recommendations. In this work, we illustrate our review of different CF techniques in the context of the Computational Intelligence Lab (CIL) CF project at ETH Z\"urich. After evaluating the performances of the individual models, we show that blending factorization-based and similarity-based approaches can lead to a significant error decrease (-9.4%) on the best-performing stand-alone model. Moreover, we propose a novel stochastic extension of a similarity model, SCSR, which consistently reduce the asymptotic complexity of the original algorithm.

Since the development of writing 5000 years ago, human-generated data gets produced at an ever-increasing pace. Classical archival methods aimed at easing information retrieval. Nowadays, archiving is not enough anymore. The amount of data that gets generated daily is beyond human comprehension, and appeals for new information retrieval strategies. Instead of referencing every single data piece as in traditional archival techniques, a more relevant approach consists in understanding the overall ideas conveyed in data flows. To spot such general tendencies, a precise comprehension of the underlying data generation mechanisms is required. In the rich literature tackling this problem, the question of information interaction remains nearly unexplored. First, we investigate the frequency of such interactions. Building on recent advances made in Stochastic Block Modelling, we explore the role of interactions in several social networks. We find that interactions are rare in these datasets. Then, we wonder how interactions evolve over time. Earlier data pieces should not have an everlasting influence on ulterior data generation mechanisms. We model this using dynamic network inference advances. We conclude that interactions are brief. Finally, we design a framework that jointly models rare and brief interactions based on Dirichlet-Hawkes Processes. We argue that this new class of models fits brief and sparse interaction modelling. We conduct a large-scale application on Reddit and find that interactions play a minor role in this dataset. From a broader perspective, our work results in a collection of highly flexible models and in a rethinking of core concepts of machine learning. Consequently, we open a range of novel perspectives both in terms of real-world applications and in terms of technical contributions to machine learning.

The emerging meta- and multi-verse landscape is yet another step towards the more prevalent use of already ubiquitous online markets. In such markets, recommender systems play critical roles by offering items of interest to the users, thereby narrowing down a vast search space that comprises hundreds of thousands of products. Recommender systems are usually designed to learn common user behaviors and rely on them for inference. This approach, while effective, is oblivious to subtle idiosyncrasies that differentiate humans from each other. Focusing on this observation, we propose an architecture that relies on common patterns as well as individual behaviors to tailor its recommendations for each person. Simulations under a controlled environment show that our proposed model learns interpretable personalized user behaviors. Our empirical results on Nielsen Consumer Panel dataset indicate that the proposed approach achieves up to 27.9% performance improvement compared to the state-of-the-art.