It includes 3,000 AI-generated videos that were made using various publicly available algorithms. The context: Over the past year, generative algorithms have become so good at synthesizing media that what they produce could soon become indistinguishable from reality. Experts are now racing to find better methods for detecting these so-called deepfakes, especially with the 2020 US presidential election approaching. Deepfake drop: On Tuesday, Google released an open-source database containing 3,000 original manipulated videos as part of its effort to accelerate the development of deepfake detection tools. It worked with 28 actors to record videos of them speaking, making common expressions, and doing mundane tasks.

Developments in artificial intelligence (AI) are leading to fundamental changes in the way we live. Algorithms can already detect Parkinson's disease and cancer, and control both cars and aircraft. How will AI change our society in the future? This documentary journeys to the hot spots of AI research in Europe, the USA and China, and looks at the revolutionary developments which are currently taking place. The rapid growth of AI offers many opportunities, but also many dangers.

According to IDC, over half of the world's data was created in the last two years, yet less than two percent has been analyzed.1 This untapped data represents a potential treasure trove for enterprises which seek more comprehensive business intelligence, improved business process, and innovative ways to remain ahead of their competitors. While high performance data analytics (HPDA), simulation and modeling, visualization, and other HPC workloads offer substantial business value on their own, augmenting those workloads with artificial intelligence (AI) derives even more benefit for a corporation. Financial institutions use AI to detect fraud real-time. Energy companies can more easily interpret ground-penetrating scans to pinpoint underground fossil fuel reserves, minimizing the impact on the surrounding environment.

For humans, bugs are a pesky distraction, but for self-driving cars they can be a matter of life or death. Ford, whose self-driving cars were just deployed in Austin, Texas, has come up with a novel set of tools to keep stray insects from interfering with the crucial sensors in its self-driving cars. Developed in partnership with Argo AI, Ford's vehicles use a complex laser technology called Lidar to generate spatial information for the car's navigation system. Similar to a radar system, lidar sends out a laser light and measures small differences in the speed and angle of the light that's reflected back from the environment, as reported by VentureBeat Those measurements are then used to generate information about surrounding vehicles and objects. Predictably, the biggest challenge for the sensors aren't parked cars or irresponsible drivers but insects, which can end up smashed against the lidar sensors and throw off their readings.

The machine learning models we are going to implement are called Time Series models. These models will examine the past and look for patterns and trends to anticipate the future. Without these models, we would have to do all of those analyses ourselves and that would take just way too much time. Luckily, we can program these Time Series models in Python to do all of that work for us, which is what we will be doing today! The Time Series models that we will be using today are: SARIMA and an additive model implemented by Facebook Prophet.

Industry leaders anticipate that the use of artificial intelligence in medical imaging will have a substantial clinical impact, ushering in an opportunity to significantly improve decision support in medical image interpretation. In this post, we cover a variety of promising medical imaging applications for AI and machine learning--including diagnosing cancer and brain aneurysms--as well as recent regulatory developments. CB Insights reports that healthcare-related AI investment totaled $1.44 billion in the first half of 2019, putting investment in the space on track to surpass the prior year, in which investment reached $2.5 billion. Much of the attention to date has surrounded applications in medical imaging or radiology. The National Center for Biologic Information (NCBI) reports that publications covering AI in radiology have steeply increased in recent years.

Regular web search engines like Google may be great for finding a restaurant, but they are lousy for locating a snippet of code. In a bid to help software developers and foster innovative code search research, GitHub last week announced the CodeSearchNet Challenge in a joint effort with California-based machine learning development tools startup Weights & Biases. A large dataset and several baseline models showing the current state of the art in code search have been released to help scientists build models for the challenge. Faced with unsatisfactory code search results from natural language processing engines, researchers have in recent years been applying machine learning techniques to improve their code searches. They quickly realized however that, unlike natural language with GLUE benchmarks, there are currently no standard datasets suitable for evaluating code search processes.

No city in the world is ready for the disruption that artificial intelligence (AI) will bring. This is the conclusion of a new review by management consultancy Oliver Wyman, which considers the readiness of 105 cities to cope with AI-inspired digital change, and finds even the smartest need to make urgent and "significant improvements". The study ranks cities on four criteria: the quality of their plan (defined as'vision'); their ability to execute on it ('activation'); the quality of their talent and infrastructure ('asset base'); and how the interplay of these last two, their activation and assets, impact their overall momentum ('trajectory'). Singapore is most prepared overall, the report says, with an average score of 75.8 out of 100 across the four criteria. But the review states no city is even close to being fully prepared.

Since the consolidation of evolutionary theory in the 19th century, many scholars have believed that progress is a linear phenomenon. For it to succeed, one must be as rational as possible, make improvements every time and follow a rigorous set of rules that are known as the scientific method. During this time, certain disciplines such as the biological and physical sciences have been glorified as essential tools for human advancement-- all while leaving the arts, humanities and social sciences behind and deeming them less important for human growth. However, we are reaching a point where the traditional areas that were clearly delineated are blurring, and the once subordinated masteries that focused on the human experience are becoming essential. My background in biological anthropology has catalyzed not only a series of thoughts of possible solutions, but most importantly, a plethora of questions for the human context that lies ahead.

I have years of experience in the enterprise software space, starting in my early years as a Customer Representative for WebHelp before expanding into market development for Intel which saw me take on a more Business Development role. My roles in companies such as Intel, Softwin and ABC Data were very Business Development-orientated which meant that I had to become an expert in the technology and in understanding how that technology could solve the problems that my customers faced. It also meant that I had to build and maintain relationships, skills that have served me very well in my recent role at UiPath. Throughout my career I saw how good quality enterprise technologies can really help drive a company's competitiveness and productivity, so UiPath was a natural fit for me when I joined them in 2016. I have helped drive UiPath's expansion into ASEAN, from setting up offices in Thailand, Singapore, Malaysia, Indonesia, the Philippines, and South Korea, to hiring staff and providing strategic oversight of operations.