MOSCOW, May 8. /TASS/. Russia’s upgraded Su-25SM3 attack aircraft will get an onboard target acquisition and sighting system with artificial intelligence elements to allow pilots to strike designated targets actually without their participation, a source in the defense industry told TASS on Wednesday.
“As part of further upgrade of attack aircraft, the latest Su-25SM3 versions will be furnished with a new sighting system. It will be fully automated and a pilot will only have to select a target on the screen and all the rest will be done by artificial intelligence,” the source said.
The target acquisition system with artificial intelligence will be able to independently identify hostile targets, keep them in sight and guide missiles. The new technology has been integrated into the unified troop command and control system, which allows mapping an optimal route towards the target and the trajectory of using weapons. Upgraded attack aircraft will also be able to receive data on targets from external sources through the command and control system. Read More
Monthly Archives: May 2019
Artificial Intelligence: A Cybersecurity Solution or the Greatest Risk of All?
Artificial intelligence has, in recent years, developed rapidly, serving as the basis for numerous mainstream applications. From digital assistants to healthcare and from manufacturing to education, AI is widely considered a powerhouse that has yet to unleash its full potential. But in the face of rising cybercrime rates, one question seems especially pertinent: is AI a solution for cybersecurity, or just another threat? Read More
Artificial Intelligence (AI) Solutions on Edge Devices
Artificial Intelligence (AI) Solutions, particularly those based on Deep Learning in the areas of Computer Vision, are done in a cloud-based environment requiring heavy computing capacity.
Inference is a relatively lower compute-intensive task than training, where latency is of greater importance for providing real-time results on a model. Most inference is still performed in the cloud or on a server, but as the diversity of AI applications grows, the centralized training and inference paradigm is coming into question.
It is possible, and becoming easier, to run AI and Machine Learning with analytics at the Edge today, depending on the size and scale of the Edge site and the particular system being used. While Edge site computing systems are much smaller than those found in central data centers, they have matured, and now successfully run many workloads due to an immense growth in the processing power of today’s x86 commodity servers. It’s quite amazing how many workloads can now run successfully at the Edge. Read More
AI System Sorts News Articles By Whether or Not They Contain Actual Information
There’s a thing in journalism now where news is very often reframed in terms of personal anecdote and-or hot take. In an effort to have something new and clickable to say, we reach for the easiest, closest thing at hand, which is, well, ourselves—our opinions and experiences.
I worry about this a lot! I do it (and am doing it right now), and I think it’s not always for ill. But in a larger sense it’s worth wondering to what degree the larger news feed is being diluted by news stories that are not “content dense.” That is, what’s the real ratio between signal and noise, objectively speaking? To start, we’d need a reasonably objective metric of content density and a reasonably objective mechanism for evaluating news stories in terms of that metric. Read More
Detecting Content-dense News Texts Combining Lexical and Syntactic Features for Detecting Content-dense Texts in News
Content-dense news report important factual information about an event in direct, succinct manner. Information seeking applications such as information extraction, question answering and summarization normally assume all text they deal with is content-dense.Here we empirically test this assumption on news articles from the business, U.S. inter-national relations, sports and science journalism domains. Our findings clearly indicate that about half of the news texts in our study are in fact not content-dense and motivate the development of a supervised content-density detector. We heuristically label a large training corpus for the task and train a two-layer classifying model based on lexical and unlexicalized syntactic features. On manually annotated data, we compare the performance of domain-specific classifiers, trained on data only from a given news domain and a general classifier in which data from all four domains is pooled together. Our annotation and prediction experiments demonstrate that the concept of content density varies depending on the domain and that naive annotators provide judgement biased toward the stereotypical domain label. Domain-specific classifiers are more accurate for domains in which content-dense texts are typically fewer. Domain independent classifiers repro-duce better naive crowdsourced judgements. Classification prediction is high across all conditions, around 80%. Read More
Detecting (Un)Important Content for Single-Document News Summarization
We present a robust approach for detecting intrinsic sentence importance in news,by training on two corpora of document-summary pairs. When used for single-document summarization, our approach,combined with the “beginning of document” heuristic, outperforms a state-of-the-art summarizer and the beginning-of-article baseline in both automatic and manual evaluations. These results represent an important advance because in the absence of cross-document repetition, single document summarizers for news have not been able to consistently outperform the strong beginning-of-article baseline. Read More
Data-Driven VCs: Who Is Using AI To Be A Better (And Smarter) Investor
The ones of you who know me are very well aware that if there is something which has sort of obsessed me for the last few years, this is is definitely how to use analytics and AI to improve the venture industry.
While I tended to focus on scouting and evaluation, I learned that AI can be also used to spot general trends, identify market gaps, improve VCs portfolio management, better match co-investors and deals, gather intelligence on competitors’ landscape, identifying potential acquirers, and improve pricing models.
I have been thinking about those issues for a while now (and stay tuned because I will post over the next few months my latest research in this field), and did already write on the importance of using AI in VC, summarized some academic research on this topic, and generally wrote about AI investors and accelerators. Read More
China’s state-run press agency has created an ‘AI anchor’ to read the news
Xinhua, China’s state-run press agency, has unveiled new “AI anchors” — digital composites created from footage of human hosts that read the news using synthesized voices.
It’s not clear exactly what technology has been used to create the anchors, but they’re in line with the most recent machine learning research. It seems that Xinhua has used footage of human anchors as a base layer, and then animated parts of the mouth and face to turn the speaker into a virtual puppet. By combining this with a synthesized voice, Xinhua can program the digital anchors to read the news, far quicker than using traditional CGI. (We’ve reached out to AI experts in the field to see what their analysis is.) Read More
Artificial Intelligence Is Creating A Fake World — What Does That Mean For Humans?
“Seeing is believing” or is it? There once was a time when we could have confidence that what we saw depicted in photos and videos was real. Even when Photoshopping images became popular, we still knew that the images started as originals. Now, with advances in artificial intelligence, the world is becoming more artificial, and you can’t be sure what you see or hear is real or a fabrication of artificial intelligence and machine learning. In many cases, this technology is used for good, but now that it exists, it can also be used to deceive. Read More
How Chinese Spies Got the N.S.A.’s Hacking Tools, and Used Them for Attacks
Chinese intelligence agents acquired National Security Agency hacking tools and repurposed them in 2016 to attack American allies and private companies in Europe and Asia, a leading cybersecurity firm has discovered. The episode is the latest evidence that the United States has lost control of key parts of its cybersecurity arsenal.
Based on the timing of the attacks and clues in the computer code, researchers with the firm Symantec believe the Chinese did not steal the code but captured it from an N.S.A. attack on their own computers — like a gunslinger who grabs an enemy’s rifle and starts blasting away. Read More
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