Fake AI pictures and videos will be nearly impossible to discern from real images as the technology behind deepfakes advances, University of California, Berkeley professor says. AI-generated pictures, videos and voices -- called deepfakes -- are so believable and widely available that people will soon not be able to discern between real and manipulated media, an image analyst told Fox News. "What's important about deepfakes is not, 'oh, we can manipulate audio, images and videos'--we've always been able to do that," said Hany Farid, a professor at University of California, Berkeley's School of Information. "But we've democratized access to technology that used to be in the hands of the few, and now are in the hands of the many." "When we enter this world where any audio, image or video can be manipulated, well, then how do you believe anything?"

As advances in artificial intelligence enable increasingly capable learning-based autonomous agents, it becomes more challenging for human observers to efficiently construct a mental model of the agent's behaviour. In order to successfully deploy autonomous agents, humans should not only be able to understand the individual limitations of the agents but also have insight on how they compare against one another. To do so, we need effective methods for generating human interpretable agent behaviour summaries. Single agent behaviour summarization has been tackled in the past through methods that generate explanations for why an agent chose to pick a particular action at a single timestep. However, for complex tasks, a per-action explanation may not be able to convey an agents global strategy. As a result, researchers have looked towards multi-timestep summaries which can better help humans assess an agents overall capability. More recently, multi-step summaries have also been used for generating contrasting examples to evaluate multiple agents. However, past approaches have largely relied on unstructured search methods to generate summaries and require agents to have a discrete action space. In this paper we present an adaptive search method for efficiently generating contrasting behaviour summaries with support for continuous state and action spaces. We perform a user study to evaluate the effectiveness of the summaries for helping humans discern the superior autonomous agent for a given task. Our results indicate that adaptive search can efficiently identify informative contrasting scenarios that enable humans to accurately select the better performing agent with a limited observation time budget.

For the larger part of my SEO career, I was leading a team of a dozen marketing specialists working on multiple SaaS or affiliate projects. At one point, I asked myself whether I could utilize my data science expertise to get better marketing results. Obviously, the answer was'yes', but what surprised me was the fact that I got some outcomes far better than what I had expected. While I'm sure many SEO specialists or even advanced tools are doing what I did -- in one way or another -- I also feel the techniques I'm about to describe aren't as popular as they could be. Here is how I used machine learning to effortlessly drive organic traffic to my client's websites.

DEEPFAKE AI-generated people will be among us by 2024 and will be nearly impossible to detect, a former White House official has warned. Pictures created by artificial intelligence, increasingly smart chatbots and sophisticated deepfake videos are already becoming hard to discern from reality. The technology is only going to become more advanced - with rapid developments already smoothing out the edges and finessing the programmes. Red flags are already being raised - as some imagery created by AI can already be almost indistinguishable from the real thing apart from a few telltale inconsistencies. The pictures at the top of this article are near perfect recreations of people's faces, created using the AI driven system Generated.Photos.

How you can train a model to learn and predict unseen data? The reason why machine learning models in general are becoming smarter is due to their dependency on using labeled data to help them discern between two similar objects. However, without these labeled datasets, you will encounter major obstacles when creating the most effective and trustworthy machine-learning model. Deep learning has been widely used to solve tasks such as Computer vision using supervised learning. However, as with many things in life, it comes with restrictions.

A research group from Nagoya University in Japan has developed an artificial intelligence for analyzing cell images that uses machine learning to predict the therapeutic effect of drugs. Called in silico FOCUS, this new technology may aid in the discovery of therapeutic agents for neurodegenerative disorders such as Kennedy disease. Current treatments for neurodegenerative diseases often have harsh side effects, including sexual dysfunction and blocking muscle tissue formation. However, researchers searching for new, less harmful treatments have been hindered by the lack of effective screening technologies to discern whether a drug is effective. One promising concept is the'anomaly discrimination concept', meaning neurons that respond to treatment have slight differences in shape compared to those that do not.

Current treatments for neurodegenerative diseases often have harsh side effects, including sexual dysfunction and blocking muscle tissue formation. However, researchers searching for new, less harmful treatments have been hindered by the lack of effective screening technologies to discern whether a drug is effective. One promising concept is the'anomaly discrimination concept', meaning neurons that respond to treatment have slight differences in shape compared to those that do not. However, these subtle differences are difficult to discern with the naked eye. Current computer technologies are also too slow to perform the analysis.

Artificial intelligence (AI) is changing the way supply chains operate. By helping to automate the analysis of large data sets and making it easier to identify trends in data, AI frees up time for supply chain professionals to engage in value-added activities that only humans can carry out. For example, even the best technology is no substitute for the uniquely human relationship-building skills that support listening to stakeholders, communicating effectively with business partners, innovating and thinking strategically about how to approach challenges. Through its AI in supply chain research, APQC has found that there are at least six new skills that supply chain employees need to develop or enhance as the result of AI adoption. While some of these are technical skills, most are soft skills that help supply chain professionals to forge stronger relationships, work more effectively with partners and solve complex problems.

Counterfactual explanations focus on "actionable knowledge" to help end-users understand how a machine learning outcome could be changed to a more desirable outcome. For this purpose a counterfactual explainer needs to discover input dependencies that relate to outcome changes. Identifying the minimum subset of feature changes needed to action an output change in the decision is an interesting challenge for counterfactual explainers. The DisCERN algorithm introduced in this paper is a case-based counter-factual explainer. Here counterfactuals are formed by replacing feature values from a nearest unlike neighbour (NUN) until an actionable change is observed. We show how widely adopted feature relevance-based explainers (i.e. LIME, SHAP), can inform DisCERN to identify the minimum subset of "actionable features". We demonstrate our DisCERN algorithm on five datasets in a comparative study with the widely used optimisation-based counterfactual approach DiCE. Our results demonstrate that DisCERN is an effective strategy to minimise actionable changes necessary to create good counterfactual explanations.

Answering complex questions from long documents requires aggregating multiple pieces of evidence and then predicting the answers. In this paper, we propose a multi-hop retrieval method, DocHopper, to answer compositional questions over long documents. At each step, DocHopper retrieves a paragraph or sentence embedding from the document, mixes the retrieved result with the query, and updates the query for the next step. In contrast to many other retrieval-based methods (e.g., RAG or REALM) the query is not augmented with a token sequence: instead, it is augmented by "numerically" combining it with another neural representation. This means that model is end-to-end differentiable. We demonstrate that utilizing document structure in this was can largely improve question-answering and retrieval performance on long documents. We experimented with DocHopper on three different QA tasks that require reading long documents to answer compositional questions: discourse entailment reasoning, factual QA with table and text, and information seeking QA from academic papers. DocHopper outperforms all baseline models and achieves state-of-the-art results on all datasets. Additionally, DocHopper is efficient at inference time, being 3~10 times faster than the baselines.