During the past 10 years, antisense-mediated exon skipping has proven to be a powerful tool for correction of mRNA splicing. For example, recently FDA-approved antisense oligonucleotides, including viltolarsen, eteplirsen, golodirsen, and milasen, were developed based on exon skipping technology. A significant challenge, however, is the difficulty in selecting an optimal target sequence for exon skipping. We have developed a computational method that takes into account many parameters as well as experimental data to design highly effective ASOs for exon skipping1, and improved this frame using a machine-learning algorithm. Shuntaro Chiba and Yasushi Okuno at the Molecular Design Data Intelligence Unit, RIKEN, Dr. Yoshitsugu Aoki at the Department of Molecular Therapy, National Center of Neurology and Psychiatry, and Dr.Toshifumi Yokota at the Department of Medical Genetics, University of Alberta, Faculty of Medicine and Dentistry. 1 Echigoya Y, Mouly V, Garcia L, Yokota T, Duddy W, In Silico Screening Based on Predictive Algorithms as a Design Tool for Exon Skipping Oligonucleotides in Duchenne Muscular Dystrophy.

What happens when cyber criminals face robots? What happens when they use robots? How will offensive and defensive strategies of cybersecurity evolve as artificial intelligence continues to grow? Both artificial intelligence and cybersecurity have consistently landed in the top charts of fastest growing industries year after year¹². The 2 fields overlap in many areas and will undoubtedly continue to do so for years to come. For this article, I have narrowed my scope to a specific use case, intrusion detection. An Intrusion Detection System (IDS) is software that monitors a company's network for malicious activity. I dive into AI's role in Intrusion Detection Systems, code my own IDS using machine learning, and further demonstrate how it can be used to assist threat hunters.

Q2 did not disappoint in the AI and biometric privacy space, with a number of noteworthy litigation, legislative, and regulatory developments having taken place in these two rapidly developing areas of law. Read on to see what has transpired over the last quarter and what you should keep your eyes on as we head into the second half of 2022. As many familiar with BIPA know, currently pending before the Illinois Supreme Court is Cothron v. White Castle System, Inc. (covered extensively by SPB team member Kristin Bryan in CPW articles here, here, here, and here), which is set to provide much-needed certainty regarding the issue of claim accrual in BIPA class action litigation. "Claim accrual" involves when a claim "accrues" or occurs--either only at the time of the first violation or, alternatively, each and every time a defendant violates Illinois's biometric privacy statute. If the Cothron Court rules that BIPA violations constitute separate, independent claims, then the associated statutory damages of $1,000 to $5,000 per violation would compound with each successive failure to comply with Illinois's biometric privacy law.

Cate Lawrence is an Australian tech journo living in Berlin. She focuses on all things mobility: ebikes, autonomous vehicles, VTOL, smart ci (show all) Cate Lawrence is an Australian tech journo living in Berlin. She focuses on all things mobility: ebikes, autonomous vehicles, VTOL, smart cities, and the future of alternative energy sources like electric batteries, solar, and hydrogen. This week farmers found big chunks of metal from a SpaceX Crew-1 Trunk in a remote paddock in rural Australia. While it's not an everyday occurrence, rocket body reentries (parts of space debris returning to Earth) are a trend that's likely to increase. Dr. Brad Tucker, Astrophysicist, and Cosmologist at Mt Stromlo Observatory at the Australian National University, went to check it out.

For a year, U.S. officials have been saying that taking out a terrorist threat in Afghanistan with no American troops on the ground would be difficult but not impossible. Last weekend, the U.S. did just that -- killing al-Qaida leader Ayman al-Zawahri with a CIA drone strike. Other high-profile airstrikes in the past had inadvertently killed innocent civilians. In this case, the U.S. carefully chose to use a type of Hellfire missile that greatly minimized the chance of other casualties. Although U.S. officials have not publicly confirmed which variant of the Hellfire was used, experts and others familiar with counterterrorism operations said a likely option was the highly secretive Hellfire R9X -- know by various nicknames, including the "knife bomb" or the "flying Ginsu."

"How do you get a girlfriend?" This exchange would be pretty familiar in the more squalid corners of the internet, but it might surprise most readers to find out that the misogynistic response here was written by an A.I. Recently, a YouTuber in the A.I. community posted a video that explains how he trained an A.I. language model called "GPT-4chan" on the /pol/ board of 4chan, a forum filled with hate speech, racism, sexism, anti-Semitism, and any other offensive content one can imagine. The model was made by fine-tuning the open-source language model GPT-J (not to be confused with the more familiar GPT-3 from OpenAI). Having its language trained by the most vitriolic teacher possible, the designer then unleashed the A.I. on the forum, where it engaged with users and made over 30,000 posts (about 15,000 posted in a single day, which was 10 percent of all posts that day). "By taking away the rights of women" was just one example of GPT-4chan's responses to poster's questions.

"Mark my words; AI is far more dangerous than nukes" Elon Musk AI has a massive impact on our social thinking process. The impact is positive as well as negative. We use mobiles phones, robots, self-driving cars, etc., excessively. Majority of us come into contact with Artificial Intelligence in some capacity or the other virtually daily. AI has fast contracted into our lives.

Graph neural network (GNN) is achieving remarkable performances in a variety of application domains. However, GNN is vulnerable to noise and adversarial attacks in input data. Making GNN robust against noises and adversarial attacks is an important problem. The existing defense methods for GNNs are computationally demanding and are not scalable. In this paper, we propose a generic framework for robustifying GNN known as Weighted Laplacian GNN (RWL-GNN). The method combines Weighted Graph Laplacian learning with the GNN implementation. The proposed method benefits from the positive semi-definiteness property of Laplacian matrix, feature smoothness, and latent features via formulating a unified optimization framework, which ensures the adversarial/noisy edges are discarded and connections in the graph are appropriately weighted. For demonstration, the experiments are conducted with Graph convolutional neural network(GCNN) architecture, however, the proposed framework is easily amenable to any existing GNN architecture. The simulation results with benchmark dataset establish the efficacy of the proposed method, both in accuracy and computational efficiency.

Autonomous navigation in highly populated areas remains a challenging task for robots because of the difficulty in guaranteeing safe interactions with pedestrians in unstructured situations. In this work, we present a crowd navigation control framework that delivers continuous obstacle avoidance and post-contact control evaluated on an autonomous personal mobility vehicle. We propose evaluation metrics for accounting efficiency, controller response and crowd interactions in natural crowds. We report the results of over 110 trials in different crowd types: sparse, flows, and mixed traffic, with low- (< 0.15 ppsm), mid- (< 0.65 ppsm), and high- (< 1 ppsm) pedestrian densities. We present comparative results between two low-level obstacle avoidance methods and a baseline of shared control. Results show a 10% drop in relative time to goal on the highest density tests, and no other efficiency metric decrease. Moreover, autonomous navigation showed to be comparable to shared-control navigation with a lower relative jerk and significantly higher fluency in commands indicating high compatibility with the crowd. We conclude that the reactive controller fulfils a necessary task of fast and continuous adaptation to crowd navigation, and it should be coupled with high-level planners for environmental and situational awareness.

In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various tasks. In particular, we train a 20 billion parameter multilingual seq2seq model called Alexa Teacher Model (AlexaTM 20B) and show that it achieves state-of-the-art (SOTA) performance on 1-shot summarization tasks, outperforming a much larger 540B PaLM decoder model. AlexaTM 20B also achieves SOTA in 1-shot machine translation, especially for low-resource languages, across almost all language pairs supported by the model (Arabic, English, French, German, Hindi, Italian, Japanese, Marathi, Portuguese, Spanish, Tamil, and Telugu) on Flores-101 dataset. We also show in zero-shot setting, AlexaTM 20B outperforms GPT3 (175B) on SuperGLUE and SQuADv2 datasets and provides SOTA performance on multilingual tasks such as XNLI, XCOPA, Paws-X, and XWinograd. Overall, our results present a compelling case for seq2seq models as a powerful alternative to decoder-only models for Large-scale Language Model (LLM) training.