This work addresses the timely yet underexplored problem of performing inference and finetuning of a proprietary LLM owned by a model provider entity on the confidential/private data of another data owner entity, in a way that ensures the confidentiality of both the model and the data. Hereby, the finetuning is conducted offsite, i.e., on the computation infrastructure of a third-party cloud provider. We tackle this problem by proposing ObfuscaTune, a novel, efficient and fully utility-preserving approach that combines a simple yet effective obfuscation technique with an efficient usage of confidential computing (only 5% of the model parameters are placed on TEE). We empirically demonstrate the effectiveness of ObfuscaTune by validating it on GPT-2 models with different sizes on four NLP benchmark datasets. Finally, we compare to a na\"ive version of our approach to highlight the necessity of using random matrices with low condition numbers in our approach to reduce errors induced by the obfuscation.

The stringent requirements for the Deep Neural Networks (DNNs) accelerator's reliability stand along with the need for reducing the computational burden on the hardware platforms, i.e. reducing the energy consumption and execution time as well as increasing the efficiency of DNN accelerators. Moreover, the growing demand for specialized DNN accelerators with tailored requirements, particularly for safety-critical applications, necessitates a comprehensive design space exploration to enable the development of efficient and robust accelerators that meet those requirements. Therefore, the trade-off between hardware performance, i.e. area and delay, and the reliability of the DNN accelerator implementation becomes critical and requires tools for analysis. This paper presents a comprehensive methodology for exploring and enabling a holistic assessment of the trilateral impact of quantization on model accuracy, activation fault reliability, and hardware efficiency. A fully automated framework is introduced that is capable of applying various quantization-aware techniques, fault injection, and hardware implementation, thus enabling the measurement of hardware parameters. Moreover, this paper proposes a novel lightweight protection technique integrated within the framework to ensure the dependable deployment of the final systolic-array-based FPGA implementation. The experiments on established benchmarks demonstrate the analysis flow and the profound implications of quantization on reliability, hardware performance, and network accuracy, particularly concerning the transient faults in the network's activations.

Recent years witnessed a rising interest towards Datalog-based ontological reasoning systems, both in academia and industry. These systems adopt languages, often shared under the collective name of Datalog$+/-$, that extend Datalog with the essential feature of existential quantification, while introducing syntactic limitations to sustain reasoning decidability and achieve a good trade-off between expressive power and computational complexity. From an implementation perspective, modern reasoners borrow the vast experience of the database community in developing streaming-based data processing systems, such as volcano-iterator architectures, that sustain a limited memory footprint and good scalability. In this paper, we focus on two extremely promising, expressive, and tractable languages, namely, Shy and Warded Datalog$+/-$. We leverage their theoretical underpinnings to introduce novel reasoning techniques, technically, "chase variants", that are particularly fit for efficient reasoning in streaming-based architectures. We then implement them in Vadalog, our reference streaming-based engine, to efficiently solve ontological reasoning tasks over real-world settings.

Because Allen used AI as a tool in the creation of the prize-winning image. Allen's final image applied AI software several times before coming up with the final picture he presented to the art competition. Allen said, "I made the prompt [to the AI program], I fine-tuned it for many weeks, curated all the images." Allen claims to have gone through 900 iterations before the final submission. This is a lot more than the 409 tries used to perfect Formula 409TM or the 40 iterations needed to find a successful formula for WD-40 TM.

The Rensselaer-IBM Artificial Intelligence Research Collaboration advances breakthroughs in more robust and secure AI 11 de November de 2020 Launched in 2018, the Rensselaer-IBM Artificial Intelligence Research Collaboration (AIRC) is a multi-year, multi-million dollar joint venture boasting dozens of ongoing projects in 2020-2021 involving more than 80 IBM and RPI researchers working to advance AI. Launched in 2018, the Rensselaer-IBM Artificial Intelligence Research Collaboration (AIRC) is a multi-year, multi-million dollar joint venture boasting dozens of ongoing projects in 2020-2021 involving more than 80 IBM and RPI researchers working to advance AI.

Search and recommendation systems, such as search engines, recruiting tools, online marketplaces, news, and social media, output ranked lists of content, products, and sometimes, people. Credit ratings, standardized tests, risk assessments output only a score, but are also used implicitly for ranking. Bias in such ranking systems, especially among the top ranks, can worsen social and economic inequalities, polarize opinions, and reinforce stereotypes. On the other hand, a bias correction for minority groups can cause more harm if perceived as favoring group-fair outcomes over meritocracy. In this paper, we study a trade-off between individual fairness and group fairness in ranking. We define individual fairness based on how close the predicted rank of each item is to its true rank, and prove a lower bound on the trade-off achievable for simultaneous individual and group fairness in ranking. We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous individual and group fairness guarantees comparable to the lower bound we prove. Our algorithm can be used to both pre-process training data as well as post-process the output of existing ranking algorithms. Our experimental results show that our algorithm performs better than the state-of-the-art fair learning to rank and fair post-processing baselines.

President Trump states after Davos " Elon Musk is the Thomas Edison of our time, he needs be protected". Elon is needed by the military and U.S against China's advancements to the free world. Cyrus A. Parsa, wishes the best for him, the U.S and the worlds people. Yet, he must cut off all ties with China for the worlds safety, and make sure his advancements in AI technology do not endanger to the worlds people or the worlds governments beyond Artificial Narrow Intelligence.

They call it the "lights out factory." A manufacturing complex run by the Japanese company FANUC, it spans 22 facilities producing 23,000 computer parts each month for companies like Tesla and Apple. The plant runs close to 24 hours a day, every day of the year. Bill de Blasio is the mayor of New York City and a candidate for the 2020 Democratic presidential nomination. The complex seems to run so smoothly, it might take a moment to realize that something's missing: human workers. FANUC's factory is 100 percent automated, with robots going "unsupervised" by a human for as many as 30 days at a time.

"I have songwriting credits…even though I don't know how to write a song." 1 The speaker of this statement is not a musician and has no musical training. He helped create an app called Endel, which is self-described as "a cross-platform audio ecosystem." 2 Endel is part of a larger part of the current hot debate over works of art being "created" by computers using programs employing "artificially intelligent" modes of computer learning, or AI for short. "Dmitry Evgrafov, Endel's composer and head of sound design, says all 600 tracks were made'with a click of a button.' There was minimal human involvement outside of chopping up the audio and mastering it for streaming. Endel even hired a third-party company to write the track titles." 3 What makes this notable is that Endel has a record deal with Warner Bros. Music. 4 "Five Endel albums have already been released, and 15 more are coming this year -- all of which will be generated by code. In the future, Endel will be able to make infinite ambient tracks." 5 But didn't the Endel engineers create the software in question?