As the deployment of NLP systems in critical applications grows, ensuring the robustness of large language models (LLMs) against adversarial attacks becomes increasingly important. Large language models excel in various NLP tasks but remain vulnerable to low-cost adversarial attacks. Focusing on the domain of conversation entailment, where multi-turn dialogues serve as premises to verify hypotheses, we fine-tune a transformer model to accurately discern the truthfulness of these hypotheses. Adversaries manipulate hypotheses through synonym swapping, aiming to deceive the model into making incorrect predictions. To counteract these attacks, we implemented innovative fine-tuning techniques and introduced an embedding perturbation loss method to significantly bolster the model's robustness. Our findings not only emphasize the importance of defending against adversarial attacks in NLP but also highlight the real-world implications, suggesting that enhancing model robustness is critical for reliable NLP applications.

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Insight Global is hiring a Data Scientist for one of our top Agriculture companies that we partner closely with. This resource will be tasked with working cross-functionally with the R&D team to develop complex ML algorithms for plant phenotype Image analysis. The Data scientist candidate should be capable of creating end to end machine learning solutions and have an in-depth understanding of image analysis practices. In this role as a Data Scientist the candidate will be working extensively with Python, TensorFlow and PyTorch. We are looking for a candidate to have obtained a Masters/PHD in Data Science or related field or have 1 years of professional experience.

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The MIT Laboratory for Information and Decision Systems (LIDS) at the MIT Institute for Data, Systems, and Society (IDSS) and the MIT Schwarzman College of Computing is seeking applicants for a Postdoctoral Scholar to perform independent research in the broad areas of machine learning and intelligent systems, mentored by Prof. Navid Azizan (azizan.mit.edu). We are looking for candidates with proven excellence in research who have the vision and interest to contribute to interdisciplinary research on foundations of deep learning, optimization, dynamical systems, control, and autonomy, with applications to robotics, autonomous systems, smart grids, and societal networks. The position is available immediately with a start date of September 1, 2022, or earlier. Candidates who are currently interviewing for faculty positions but would like to spend a year at MIT before starting their faculty careers are especially encouraged to apply. Job Requirements: Doctoral degree (expected or obtained) in engineering, computer science, data science, operations research, mathematics, or a related field; strong analytical and written communication skills; and ability to work effectively in an interdisciplinary environment.

Thou shalt not exploit workers in the 4th Industrial Revolution. The Great Resignation has opened people's eyes as to what is possible when workers push back--when they say enough is enough. Since its start, more than 50 million Americans have voluntarily left their jobs. Many cited dehumanizing conditions and lack of respect from their employer as reasons. Want your new hires to feel like more than cogs in the machine?

Internships are a great way to get hands-on, practical experience in the fields of Machine Learning, Artificial Intelligence, and Data Science. Those looking for internships in these, and related fields, should explore the options below. Be sure to note requirements–some internships have minimum lengths and only accept those pursuing higher educational degrees while others offer rolling acceptance for a range of experiences and backgrounds. All of them, however, will provide interns an opportunity to apply their theoretical and practical skills to real word, tangible scenarios. Facebook's Meta is hiring for a group of data science interns interested in learning more about using Data Science for more effective advertising, marketing, and sales applications.

Data Science Interviews cover a wide range of topics: a Statistics component testing candidates' general statistical fluency, a Machine Learning section on candidates' knowledge of various ML algorithms and the tradeoffs, and a programming part that excruciates us for live-coding skills. Even more, there is a newly found trend in the DS community, which is shifting towards Computer Science fundamentals like Data Structure & Algorithm (Check the wonderful article Emma Ding @Airbnb). Taken together, they appear to be too daunting to handle. If we divide the big chunks into smaller pieces, each piece becomes more "chewable" and manageable. This is the first chapter of a dual post on fundamental concepts.