Tensorflow 2.0: Deep Learning and Artificial Intelligence

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Roberto G.E. Martín on LinkedIn: #AI #ReinforcementLearning

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Using just a depth-sensing camera, GPS, and compass data, the AI Agent gets its goal 99.9% of the time along a route that is very close to the shortest possible path, which means no wrong turns, no backtracking, and no exploration.


AI and Data Are Reshaping Insurance

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In the digital era of innovative products and services, Insurtech technologies are bringing great opportunities to the insurance sector and accelerating the industry's transformation. Advances in AI and data science are leading insurers toward the effective use of machine learning, data modeling and predictive analytics to improve back-end processes and streamlining and automation of the front-end experience for both consumers and insurance companies. Customers are acquiring insurance policies much faster and easier with the help of automated processes. These technologies differ depending on the systems that employ them and the people they serve. Integration gateways relying on data and AI are creating new customer experiences.


People can now be identified at a distance by their heartbeat

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BEFORE PULLING the trigger, a sniper planning to assassinate an enemy operative must be sure the right person is in the cross-hairs. Western forces commonly use software that compares a suspect's facial features or gait with those recorded in libraries of biometric data compiled by police and intelligence agencies. Such technology can, however, be foiled by a disguise, head-covering or even an affected limp. For this reason America's Special Operations Command (SOC), which oversees the units responsible for such operations in the various arms of America's forces, has long wanted extra ways to confirm a potential target's identity. Responding to a request from SOC, the Combating Terrorism Technical Support Office (CTTSO), an agency of the defence department, has now developed a new tool for the job.




Amazon's AI automatically dubs videos into other languages

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They say that it improves the perceived naturalness of dubbing and highlights the relative importance of each proposed step. As the paper's coauthors note, automatic dubbing involves transcribing speech to text and translating that text into another language before generating speech from the translated text.


Facebook AI gives maps the brushoff in helping robots find the way

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Facebook has scored an impressive feat involving AI that can navigate without any map. Facebook's wish for bragging rights, although they said they have a way to go, were evident in its blog post, "Near-perfect point-goal navigation from 2.5 billion frames of experience." Long story short, Facebook has delivered an algorithm that, quoting MIT Technology Review, lets robots find the shortest route in unfamiliar environments, opening the door to robots that can work inside homes and offices." And, in line with the plain-and-simple, Ubergizmo's Tyler Lee also remarked: "Facebook believes that with this new algorithm, it will be capable of creating robots that can navigate an area without the need for maps...in theory, you could place a robot in a room or an area without a map and it should be able to find its way to its destination." Erik Wijmans and Abhishek Kadian in the Facebook Jan. 21 post said that, well, after all, one of the technology key challenges is "teaching these systems to navigate through complex, unfamiliar real-world environments to reach a specified destination--without a preprovided map." Facebook has taken on the challenge. The two announced that Facebook AI created a large-scale distributed reinforcement learning algorithm called DD-PPO, "which has effectively solved the task of point-goal navigation using only an RGB-D camera, GPS, and compass data," they wrote. DD-PPO stands for decentralized distributed proximal policy optimization. This is what Facebook is using to train agents and results seen in virtual environments such as houses and office buildings were encouraging. The bloggers pointed out that "even failing 1 out of 100 times is not acceptable in the physical world, where a robot agent might damage itself or its surroundings by making an error." Beyond DD-PPO, the authors gave credit to Facebook AI's open source AI Habitat platform for its "state-of-the-art speed and fidelity." AI Habitat made its open source announcement last year as a simulation platform to train embodied agents such as virtual robots in photo-realistic 3-D environments. Facebook said it was part of "Facebook AI's ongoing effort to create systems that are less reliant on large annotated data sets used for supervised training." InfoQ had said in July that "The technology was taking a different approach than relying upon static data sets which other researchers have traditionally used and that Facebook decided to open-source this technology to move this subfield forward." Jon Fingas in Engadget looked at how the team worked toward AI navigation (and this is where that 25 billion number comes in). "Previous projects tend to struggle without massive computational power.


3DPeople First Dataset to Map Clothing Geometry

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Recent progress in the field of 3D human shape estimation enables the efficient and accurate modeling of naked body shapes, but doesn't do so well when tasked with displaying the geometry of clothes. A team of researchers from Institut de Robòtica i Informàtica Industrial and Harvard University recently introduced 3DPeople, a large-scale comprehensive dataset with specific geometric shapes of clothes that is suitable for many computer vision tasks involving clothed humans. In addition to the new dataset, researchers also developed a novel shape parameterization algorithm and a multi-resolution end-to-end deep generative network for predicting dressed body shape. Researchers used four cameras to capture each subject's action sequence. In addition to providing textured 3D meshes for the clothing and bodies, researchers also annotated the dataset with RGB (one of the most widely used color systems, including almost all colors that can be perceived by human vision), 3D skeleton, depth, optical flow, and semantic information (body parts and cloth labels).


The battle for ethical AI at the world's biggest machine-learning conference

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Facial-recognition algorithms have been at the centre of privacy and ethics debates.Credit: Qilai Shen/Bloomberg/Getty Diversity and inclusion took centre stage at one of the world's major artificial-intelligence (AI) conferences in 2018. But once a meeting with a controversial reputation, last month's Neural Information Processing Systems (NeurIPS) conference in Vancouver, Canada, saw attention shift to another big issue in the field: ethics. The focus comes as AI research increasingly deals with ethical controversies surrounding the application of its technologies -- such as in predictive policing or facial recognition. Issues include tackling biases in algorithms that reflect existing patterns of discrimination in data, and avoiding affecting already vulnerable populations. "There is no such thing as a neutral tech platform," warned Celeste Kidd, a developmental psychologist at University of California, Berkeley, during her NeurIPS keynote talk about how algorithms can influence human beliefs.