Semantic representation and inference is essential for Natural Language Processing (NLP). The state of the art for semantic representation and inference is deep learning, and particularly Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and transformer Self-Attention models. This thesis investigates the use of deep learning for novel semantic representation and inference, and makes contributions in the following three areas: creating training data, improving semantic representations and extending inference learning. In terms of creating training data, we contribute the largest publicly available dataset of real-life factual claims for the purpose of automatic claim verification (MultiFC), and we present a novel inference model composed of multi-scale CNNs with different kernel sizes that learn from external sources to infer fact checking labels. In terms of improving semantic representations, we contribute a novel model that captures non-compositional semantic indicators. By definition, the meaning of a non-compositional phrase cannot be inferred from the individual meanings of its composing words (e.g., hot dog). Motivated by this, we operationalize the compositionality of a phrase contextually by enriching the phrase representation with external word embeddings and knowledge graphs. Finally, in terms of inference learning, we propose a series of novel deep learning architectures that improve inference by using syntactic dependencies, by ensembling role guided attention heads, incorporating gating layers, and concatenating multiple heads in novel and effective ways. This thesis consists of seven publications (five published and two under review).

State-of-the-art (SOTA) Generative Models (GMs) can synthesize photo-realistic images that are hard for humans to distinguish from genuine photos. We propose to perform reverse engineering of GMs to infer the model hyperparameters from the images generated by these models. We define a novel problem, "model parsing", as estimating GM network architectures and training loss functions by examining their generated images -- a task seemingly impossible for human beings. To tackle this problem, we propose a framework with two components: a Fingerprint Estimation Network (FEN), which estimates a GM fingerprint from a generated image by training with four constraints to encourage the fingerprint to have desired properties, and a Parsing Network (PN), which predicts network architecture and loss functions from the estimated fingerprints. To evaluate our approach, we collect a fake image dataset with $100$K images generated by $100$ GMs. Extensive experiments show encouraging results in parsing the hyperparameters of the unseen models. Finally, our fingerprint estimation can be leveraged for deepfake detection and image attribution, as we show by reporting SOTA results on both the recent Celeb-DF and image attribution benchmarks.

If you use such social media websites as Facebook and Twitter, you may have come across posts flagged with warnings about misinformation. So far, most misinformation – flagged and unflagged – has been aimed at the general public. Imagine the possibility of misinformation – information that is false or misleading – in scientific and technical fields like cybersecurity, public safety and medicine. There is growing concern about misinformation spreading in these critical fields as a result of common biases and practices in publishing scientific literature, even in peer-reviewed research papers. As a graduate student and as facultymembers doing research in cybersecurity, we studied a new avenue of misinformation in the scientific community.

At the beginning of June, the Armenian Prime Minister, Tigran Avinyan, spoke about the artificial intelligence strategy that Armenia wishes to put in place. Several topics were discussed: fundamental research, applied research, infrastructure, public sector, private sector, training and financing. On the Yerevan side, we now wish to give priority to AI in order to join, in the long term, the countries already well advanced in the sector. In the state of play mentioned by the Prime Minister of Armenia, he wants to focus his strategy on basic research, which he believes would be the point requiring the least investment: a stable internet connection and supercomputers may be enough to conduct research or training on artificial neural networks. The government also wants to build on its strengths in mathematics and experimental and natural sciences.

Levi's loyalty program, which launched in 2020, has built a customer pool that now includes 5 million members; using AI, this facet of the company's business is more personalized to each client than ever before. A company that has followed a progressive course over its 168-year history, Levi Strauss & Co. has played an important role during revolutionary moments within history. From creating an integrated employment force in the mid–20th century or ensuring greater supply-chain transparency in the 1990s to encouraging United States citizens to vote in 2020, the San Francisco–based denim leader has remained committed to progress. This part of the brand's mission made it a perfect fit for Chief Global Strategy and Artificial Intelligence Officer Katia Walsh, who considers herself to be an unlikely fashion professional but has felt aligned with Levi's principles. As a student journalist growing up in communist Bulgaria, Walsh was reprimanded in school at 15 years old for writing a story that displeased local officials.

Organizations of all sizes have accelerated the rate at which they employ AI models to advance digital business transformation initiatives. But in the absence of any clear-cut regulations, many of these organizations don't know with any certainty whether those AI models will one day run afoul of new AI regulations. Ted Kwartler, vice president of Trusted AI at DataRobot, talked with VentureBeat about why it's critical for AI models to make predictions "humbly" to make sure they don't drift or, one day, potentially run afoul of government regulations. This interview has been edited for brevity and clarity. VentureBeat: Why do we need AI to be humble?

Recently, TikTok made a change to its U.S. privacy policy, allowing the company to "automatically" collect new types of biometric data, including what it describes as "faceprints" and "voiceprints." TikTok's unclear intent, the permanence of the biometric data and potential future uses for it have caused concern among experts who say users' security and privacy could be at risk. On June 2, TikTok updated the "Information we collect automatically" portion of its privacy policy to include a new section called "Image and Audio Information," giving itself permission to gather certain physical and behavioral characteristics from its users' content. The increasingly popular video sharing app may now collect biometric information such as "faceprints and voiceprints," but the update doesn't define these terms or what the company plans to do with the data. "Generally speaking, these policy changes are very concerning," Douglas Cuthbertson, a partner in Lieff Cabraser's Privacy & Cybersecurity practice group, tells TIME.

The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.

Tokyo stocks turned up Monday, getting a boost from a continued rally on Wall Street last week. The 225-issue Nikkei average of the Tokyo Stock Exchange rose 213.07 points, or 0.74%, to close at 29,161.80, The Topix index of all first section issues ended 5.73 points, or 0.29%, higher at 1,959.75, snapping its three-day losing streak. The Tokyo market got off to a strong start, after all three U.S. market gauges including the Dow Jones Industrial Average extended their gains Friday thanks to data showing improvement in consumer confidence in the United States in June. Although selling to lock in gains from the initial market spurt gathered steam in the morning, stocks gradually extended gains in the afternoon in pace with Dow futures in off-hours trading. Trading was generally lackluster with many players taking to the sidelines ahead of the U.S. Federal Reserve's two-day Federal Open Market Committee meeting from Tuesday, brokers said.

Content monitoring through AI technologies, smart cameras for facial identification, DNA profiling algorithms are some of the techniques witnessing a surge throughout the world. Technologies provide us with reliable and trustable data to bank upon, but the questions arising on its accuracy can be a debatable issue. Let's have a look at a recent case to understand the apprehension. Recently, in two separate judgements -- a judge from the Appellate Division of the Superior Court of New Jersey and a federal judge in Pennsylvania in the United States have ordered the prosecutor to hand over the source code of TrueAllele by Cybergenetics. The software program ran different DNA data available on a gun through complex statistical algorithms to compare the probability of a specific person's DNA being present.