AI-assisted reverse engineering (AIARE) is a branch of computer science that leverages artificial intelligence (AI), notably machine learning (ML) strategies, to augment and automate the process of reverse engineering. The latter involves breaking down a product, system, or process to comprehend its structure, design, and functionality. AIARE was primarily introduced in the early years of the 21st century, witnessing substantial advancements from the mid-2010s onwards. == Overview == Conventionally, reverse engineering is conducted by specialists who dismantle a system to grasp its working principles, often for the purposes of reproduction, modification, enhancement of compatibility, or forensic examination. This method, while efficient, can be laborious and time-intensive, particularly when dealing with intricate software or hardware systems. AIARE integrates machine learning algorithms to either partially automate or augment this process. It is capable of detecting patterns, relationships, structures, and potential vulnerabilities within the analyzed system, frequently surpassing human experts in speed and accuracy. This has rendered AIARE a critical tool in numerous fields, including cybersecurity, software development, and hardware design and analysis. == Techniques == AIARE encompasses several AI methodologies: === Supervised learning === Supervised learning employs tagged data to train models to recognize system components, their operations, and their interconnections. This method is particularly helpful in software analysis to discover vulnerabilities or enhance compatibility. === Unsupervised learning === Unsupervised learning is utilized to detect concealed patterns and structures in untagged data. It proves beneficial in comprehending complex systems where there's no evident labeling or mapping of components. === Reinforcement learning === Reinforcement learning is employed to build models that progressively refine their system understanding through a process of trial and error. This method is often implemented when deciphering a system's functionality under various circumstances or configurations. === Deep learning === Deep learning is employed for analysis of high-dimensional data. For instance, deep learning techniques can aid in examining the layout and connections of integrated circuits (ICs), substantially reducing the manual effort required for reverse engineering. == Benefits == === Usable Security === AIARE expands usable security as reverse engineering is traditionally slow and highly specialized as it produces dense, low-level information (usually in Assembly or C) when using tools like Ghidra. The use of multiple different methods to interface with models today (such as through chat bots like ChatGPT) greatly reduces the barrier to entry by providing a clear way to interact with the user and even providing meaningful decompiled source code. In addition, either done automatically or through prompt engineering, a model is capable of producing a high-level summary and explanation of its reverse engineering efforts in human-readable form that doesn't require much knowledge on code. === Speedup === AIARE is capable of processing data much faster than humans, providing a boost in speed when analyzing said data. In the context of computer security, this can greatly speed up incident management or response and malware detection as AIARE can be automated to drastically reduce the manual effort usually associated with reverse engineering. == Limitations == In an effort to improve readability for reverse engineering, AI-generated code may introduce erroneous bugs not present in the source. This compromises the correctness of the code if not carefully validated and will throw off reverse engineering efforts. Additionally, AIARE's weakness in zero-shot prompting makes gathering accurate data without reference data in the prompt more inconsistent, thus requiring a user to provide some quality data of their own that hurts its usability.
VSCO
VSCO ( ), formerly known as VSCO Cam, is a photography mobile app available for iOS and Android devices. The app was created by Joel Flory and Greg Lutze. The VSCO app allows users to capture photos in the app and edit them, using preset filters and editing tools. == History == Visual Supply Company was founded by Joel Flory and Greg Lutze in California, in 2011. VSCO was launched in 2012. It raised $40 million from investors in May 2014. In 2017, VSCO launched a subscription model. As of 2018, Visual Supply Company has $90 million in funding from investors and over 2 million paying members. In 2019, VSCO acquired Rylo, a video editing startup founded by the original developer of Instagram’s Hyperlapse. Visual Supply Company has locations in Oakland, California, where it is headquartered, and Chicago, Illinois. In December 2020 VSCO acquired AI-powered video editing app Trash. In April 2018, VSCO reached over 30 million users. In September 2023, Eric Wittman was appointed as the new CEO and co-founder Joel Flory became executive chairman. == Usage == Users must register an account to use the app. Photos can be taken or imported from the camera roll, as well as short videos or animated GIFs (known in the app as DSCO; iOS only). The user can edit their photos through various preset filters, or through the "toolkit" feature which allows finer adjustments to fade, clarity, skin tone, tint, sharpness, saturation, contrast, temperature, exposure, and other properties. Users have the option of posting their photos to their profile, where they can also add captions and hashtags. Photos can also be exported back into the camera roll or shared with other social networking services. The users also have an option to edit their own videos from their camera roll with the VSCO yearly membership, but they are not able to post camera roll as VSCO Film X videos to their account on VSCO. JPEG and raw image files can be used. Research on image based social media platforms has found that engagement with posting, editing, and interacting with images can influence users' mood, self esteem, and body satisfaction. Studies also suggest that greater emotional investment in social media content is associated with increased negative psychological outcomes including stress and depressive symptoms. == In popular culture == VSCO's Oakland headquarters was a key filming location for Boots Riley's 2018 film Sorry to Bother You.
Is an AI Headshot Generator Worth It in 2026?
In search of the best AI headshot generator? An AI headshot generator is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI headshot generator slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
Is an AI Subtitle Generator Worth It in 2026?
Comparing the best AI subtitle generator? An AI subtitle generator is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI subtitle generator slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
Oren Etzioni
Oren Etzioni (born 1964) is Professor Emeritus of Computer Science at the University of Washington, and founding CEO of the Allen Institute for Artificial Intelligence (AI2). Etzioni is a co-founder of Vercept, an AI startup, and founder and CEO of TrueMedia.org, a non-profit dedicated to fighting political deepfakes, which launched in April 2024. He is also the Founder and Technical Director of the AI2 Incubator and a venture partner at the Madrona Venture Group. == Early life and education == Etzioni is the son of Israeli-American intellectual Amitai Etzioni. He was the first student to major in computer science at Harvard University, where he earned a bachelor's degree in 1986. He earned a PhD from Carnegie Mellon University in January, 1991, supervised by Tom M. Mitchell. == University of Washington career == Etzioni joined the University of Washington faculty in 1991, immediately after receiving his PhD. He rose through the ranks to become the Washington Research Foundation Entrepreneurship Professor in Computer Science & Engineering. Etzioni's research has been focused on basic problems in the study of intelligence, machine reading, machine learning and web search. Past projects include Internet Softbots—the study of intelligent agents in the context of real-world software testbeds. In 2003, he started the KnowItAll project for acquiring massive amounts of information from the web. In 2005, he founded and became the director of the university's Turing Center. The center investigated problems in data mining, natural language processing, the Semantic Web and other web search topics. Etzioni coined the term machine reading and helped to create the first commercial comparison shopping agent. He has published over 200 technical papers, and his H-index exceeds 100. == Entrepreneurship == As a faculty member Etzioni was also an active entrepreneur, founding multiple companies and pioneering multiple technologies including MetaCrawler (bought by Infospace), Netbot (bought by Excite in 1997 for $35 million), and ClearForest (bought by Reuters). He founded Farecast, a travel metasearch and price prediction site, which was acquired by Microsoft in 2008 for $115 million. Before founding Farecast, he developed a program originally called Hamlet, that used algorithms to identify patterns in airfare data using data-mining techniques. He also co-founded Decide.com, a website to help consumers make buying decisions using previous price history and recommendations from other users. Decide.com was bought by eBay in September, 2013. Etzioni is also a venture partner at the Madrona Venture Group. He is founder and CEO of TrueMedia.org, a non-profit dedicated to fighting political deepfakes, which launched in April 2024. Etzioni is a co-founder of Vercept, an AI startup formed in 2025. == Founding CEO of AI2 == In September 2013 Etzioni was selected as the Founding CEO of the Allen Institute for Artificial Intelligence by philanthropist Paul G. Allen, and in January 2014 he took a leave of absence from the University of Washington to serve in that role. Etzioni's technical contributions continued at AI2; for example, in 2015, he helped to create the Semantic Scholar search engine. Under Etzioni’s leadership, AI2 grew from zero to over two hundred team members including notable researchers and engineers across several domains of AI. By 2021, its AI2 researchers had published near 700 papers in publications such as AAAI, ACL, CVPR, NeurIPS, and ICLR. Twenty-four of these papers had garnered special-recognition awards. AI2 also offered several key resources and tools to the AI community including the AllenNLP library, Semantic Scholar, and the conservation platforms EarthRanger and Skylight. Ed Lazowska, AI2 Board Member, has stated about Etzioni that he "took the collegial, collaborative culture that he absorbed in his 20+ years as a professor in UW's Allen School and mixed it with the singular focus that drives startups to create an elixir that AI2 folks have been drinking over the last eight years. The result is an exceptional organization of scientists, engineers, and entrepreneurs that's pursuing Paul Allen’s vision of ‘AI for the Common Good’ with extraordinary success.” == Popular press == In addition to his scientific publications, Etzioni has written commentary on AI for The New York Times, Wired, Nature, and other publications. After reading the idea in a book about AI by Brad Smith and Harry Shum, Etzioni has attempted to create an oath for AI practitioners. In 2018, he published what he called a "Hippocratic Oath for artificial intelligence practitioners" in TechCrunch. == Awards and recognition == In 1993, Etzioni received a National Young Investigator Award. In 2003, Etzioni was elected as AAAI Fellow. In 2005, Etzioni received an IJCAI Distinguished Paper Award for "A Probabilistic Model of Redundancy in Information Extraction". In 2007, he received the Robert S. Engelmore Memorial Award. In 2012 Etzioni was featured as GeekWire's "Geek of the Week". In 2013 Etzioni was voted "Geek of the Year" through GeekWire. In 2022, Etzioni received the 2012 ACL Test-of-Time Paper Award. In 2022, Etzioni, along with Ana-Maria Popescu and Henry Kautz, received the ACM Intelligent User Interfaces Most Impact Award for their 2003 paper, "Towards a Theory of Natural Language Interfaces to Databases". == Personal life == Etzioni has three children, and has said in interviews that family is his number one priority. He is married to Ivone Etzioni, and was previously married to Dr. Ruth Etzioni, a biostatistician at the Fred Hutchinson Cancer Center. Outside of his professional career, Etzioni has a wide range of personal interests. He has attended the Burning Man festival, which he described as a valuable way to step outside his comfort zone. His first computer was a TRS-80, and he has described his car’s GPS as his favorite gadget, joking that he has “no sense of direction.” == Selected publications == === Scholarly publications === Etzioni, Oren (July 1994). "A Softbot-based Interface to the Internet" (PDF). Communications of the ACM. Retrieved March 29, 2018. Etzioni, Oren (December 2008). "Open Information Extraction from the Web" (PDF). Communications of the ACM. Retrieved March 29, 2018. Zamir, Oren; Etzioni, Oren (1998). "Web document clustering". Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. ACM. pp. 46–54. doi:10.1145/290941.290956. ISBN 978-1-58113-015-7. S2CID 244069. Zamir, Oren; Etzioni, Oren (May 1999). "Grouper: a dynamic clustering interface to Web search results". Computer Networks. 31 (11–16): 1361–1374. CiteSeerX 10.1.1.31.8216. doi:10.1016/S1389-1286(99)00054-7. S2CID 206134308. Popescu, Ana-Maria; Etzioni, Oren (2005). "Extracting product features and opinions from reviews". Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05. pp. 339–346. doi:10.3115/1220575.1220618. Etzioni, Oren; Cafarella, Michael; Downey, Doug; Popescu, Ana-Maria; Shaked, Tal; Sonderland, Stephen; Weld, Daniel; Yates, Alexander (June 2005). "Unsupervised named-entity extraction from the Web: An experimental study". Artificial Intelligence. 165 (1): 91–134. doi:10.1016/j.artint.2005.03.001. Downey, Doug; Etzioni, Oren; Sonderland, Stephen (July 2010). "Grouper: Analysis of a probabilistic model of redundancy in unsupervised information extraction". Artificial Intelligence. 174 (11): 726–748. CiteSeerX 10.1.1.174.2441. doi:10.1016/j.artint.2010.04.024. === Popular articles === Etzioni, Oren (August 4, 2011). "Web Search Needs a Shakeup" (PDF). Nature. Retrieved November 21, 2019. Etzioni, Oren (December 9, 2014). "AI Won't Exterminate Us – It Will Empower Us". Backchannel. Retrieved March 29, 2018. Etzioni, Oren (February 4, 2016). "To Keep AI Safe -- Use AI". Vox. Retrieved November 21, 2019. Etzioni, Oren (April 8, 2016). "Quora Session with Oren Etzioni". Quora. Retrieved March 29, 2018. Etzioni, Oren (June 15, 2016). "Deep Learning Isn't a Dangerous Magic Genie. It's Just Math". Wired. Retrieved March 29, 2018. Etzioni, Oren (September 20, 2016). "No, the Experts Don't Think Superintelligent AI is a Threat to Humanity". MIT Technology Review. Retrieved November 21, 2019. Etzioni, Oren (July 6, 2017). "Artificial intelligence: AI Zooms in on highly influential citations". Nature. Retrieved March 29, 2018. Etzioni, Oren (September 1, 2017). "How to Regulate Artificial Intelligence". The New York Times. Retrieved March 29, 2018. Etzioni, Oren (November 2, 2017). "Workers Displaced by Automation Should Try A New Job: Caregiver". Wired. Retrieved March 29, 2018. Etzioni, Oren (March 14, 2018). "A Hippocratic Oath for artificial intelligence practitioners". Tech Crunch. Retrieved March 29, 2018. Etzioni, Oren (March 7, 2018). "A 'Manhattan Project' for science research". The Hill. Retrieved November 21, 2019. Etzioni, Ore
Shadow and highlight enhancement
Shadow and highlight enhancement refers to an image processing technique used to correct exposure. The use of this technique has been gaining popularity, making its way onto magazine covers, digital media, and photos. It is, however, considered by some to be akin to other destructive Photoshop filters, such as the Watercolor filter, or the Mosaic filter. == Shadow recovery == A conservative application of the shadow/highlight tool can be very useful in recovering shadows, though it tends to leave a telltale halo around the boundary between highlight and shadow if used incorrectly. A way to avoid this is to use the bracketing technique, although this usually requires a tripod. == Highlight recovery == Recovering highlights with this tool, however, has mixed results, especially when using it on images with skin in them, and often makes people look like they have been "sprayed with fake tan". == Shadow brightening - manual == One way to brighten shadows in image editing software such as GIMP or Adobe Photoshop is to duplicate the background layer, invert the copy and set the blend modes of that top layer to "Soft Light". You can also use an inverted black and white copy of the image as a mask on a brightening layer, such as Curves or Levels. == Shadow brightening - automatic == Several automatic computer image processing-based shadow recovery and dynamic range compression methods can yield a similar effect. Some of these methods include the retinex method and homomorphic range compression. The retinex method is based on work from 1963 by Edwin Land, the founder of Polaroid. Shadow enhancement can also be accomplished using adaptive image processing algorithms such as adaptive histogram equalization or contrast limiting adaptive histogram equalization (CLAHE).
Zoubin Ghahramani
Zoubin Ghahramani FRS (Persian: زوبین قهرمانی; born 8 February 1970) is a British-Iranian machine learning and AI researcher, Vice President of Research at Google DeepMind and Professor of Information Engineering at the University of Cambridge. He has been a Fellow of St John's College, Cambridge since 2009. He held appointments at University College London from 1998 to 2005 and was Associate Research Professor in the Machine Learning Department at Carnegie Mellon University from 2003 to 2012. He was the Chief Scientist of Uber from 2016 until 2020. He joined Google Brain in 2020 as Senior Research Director, becoming a VP of Research in 2021, and heading Google Brain until its merger with DeepMind to form Google DeepMind. He was a founding Cambridge Liaison Director of the Alan Turing Institute and also founding Deputy Director of the Leverhulme Centre for the Future of Intelligence. Ghahramani contributed to the Royal Society's Machine Learning Report in 2017 and led the UK's Future of Compute Review, in 2023. == Education == Ghahramani was educated at the American School of Madrid in Spain and the University of Pennsylvania where he was awarded a dual degree in Cognitive Science and Computer Science in 1990. He obtained his Ph.D. in Cognitive Neuroscience from the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology, supervised by Michael I. Jordan and Tomaso Poggio. == Research and career == Following his Ph.D., Ghahramani moved to the University of Toronto in 1995 as a Postdoctoral Fellow in the Artificial Intelligence Lab, working with Geoffrey Hinton. From 1998 to 2005, he was a member of the faculty at the Gatsby Computational Neuroscience Unit, University College London. Ghahramani has made significant contributions in the areas of Bayesian machine learning (particularly variational methods for approximate Bayesian inference), as well as graphical models and computational neuroscience. His current research focuses on nonparametric Bayesian modelling and statistical machine learning. He has also worked on artificial intelligence, information retrieval, bioinformatics and statistics which provide the mathematical foundations for handling uncertainty, making decisions, and designing learning systems. He has published over 300 papers, receiving over 100,000 citations (an h-index of 132). He co-founded Geometric Intelligence in 2014, with Gary Marcus, Doug Bemis, and Ken Stanley, which was acquired by Uber in 2016. Afterwards, he transferred to Uber's AI Labs in 2016, and later became VP of AI and Chief Scientist at Uber. In 2020 he joined Google and became VP of Research and head of Google Brain in 2021 until its merger with DeepMind in April 2023. == Awards and honors == Ghahramani was elected Fellow of the Royal Society (FRS) in 2015. His certificate of election reads: Zoubin Ghahramani is a world leader in the field of machine learning, significantly advancing the state-of-the-art in algorithms that can learn from data. He is known in particular for fundamental contributions to probabilistic modeling and Bayesian nonparametric approaches to machine learning systems, and to the development of approximate variational inference algorithms for scalable learning. He is one of the pioneers of semi-supervised learning methods, active learning algorithms, and sparse Gaussian processes. His development of novel infinite dimensional nonparametric models, such as the infinite latent feature model, has been highly influential.He was awarded the Royal Society Milner Award in 2021 in recognition of 'his fundamental contributions to probabilistic machine learning'.