I study a PhD in Security within Machine Learning and this is actually an extremely dangerous thing with nearly all DNN models due to how they 'see' data and is used within many ML attacks. DNN's don't see the world as we do (Obviously) but more importantly that means images or data can appear exactly the same to us, but to a DNN be completely different.You can imagine a scenario where a DNN within a autonomous car can be easily tricked to misclassify road signs. To us, a readable STOP sign with always say STOP, even if it has scratches, and dirt on the sign, we can easily interpret what the sign should be telling us. However an attacker can use noise (Similar to the photo of another road sign) to alter the image in tiny ways to cause a DNN to think a STOP sign is actually just a speed limit sign, while to us it still looks exactly like a STOP sign. Deploy such an attack on a self driving car at a junction with a stop sign and you can imagine how the car would simply drive on rather than stopping. You'll be surprised how easy it is to trick AI, even big companies like YouTube's have issues with this within copyright music detection if you perform complex ML attacks upon the music.Here's a paper similar to the scenario I described but by placing stickers in specific places to make an AI not see stop signs; https://arxiv.org/pdf/1707.08945.pdf - _Waldy_

Give us Innovative Puzzle and we will solve that puzzle. Intellectual Property Right scenario is to work in holistic view for knowledge exchange and visioning a world to solve massive issues which needs urgent attention to increase GDP of India aka Bharat. Are you looking for Chief Innovation Officier? Are you looking for Invention Harvesting and Invention Management? Doing international patent information researches, Patent Mapping to products to identify infringers, Intellectual Property Rights management for universities, drafting patent specifications, preparing response to notification of reasons for innovation refusal, go to person to facilitate patent licensing and initiating patent infringement proceedings before the Delhi High Court.

Summary – Undoubtedly, AI augmentation has become the core subject of the AI world. To be precise, augmented AI is here to show the world that cognitive ability is here just to support human intelligence and it is not here to replace it. Nonetheless, it is the role human intelligence possess using deep learning and machine learning algorithms to solve complex problems. Simply said, AI augmentation is here to make life much more simple as such to support, accelerate, and increase the efficiency of the tasks humans perform. Such instances include tasks like auto-transcription software and self-driving cars.

Deep Longevity, which specialises in the development and the application of next-generation AI for aging and longevity research, has announced the publication of an article in Nature Aging titled Artificial Intelligence in Longevity Medicine, written by Alex Zhavoronkov, Evelyne Yehudit Bischof and Kai-Fu Lee. Longevity.Technology: Longevity and AI are deeply enmeshed; from accelerating innovation and technology transfer, to developing personalised health therapies, the presence of AI is a key factor in extending lifespan and healthspan and ensuring maximum wellness. Next-generation AI could not only improve longevity investigative strategies and research, but push them in entirely new directions – vive la révolution! Hong Kong-based Deep Longevity was spun out of Insilico Medicine and quickly acquired by Regent Pacific. It develops explainable AI systems to track the rate of aging at the molecular, cellular, tissue, organ, system, physiological and psychological levels, as well as developing systems for the emerging field of longevity medicine. Creators of deep aging clocks that leverage data from multiple biomarkers, Deep Longevity, through a research partnership with Human Longevity, Inc, provides various aging clocks to physicians and researchers.

Self-driving cars can be as stubborn as a mule. Sometimes it seems as though a car is about as stubborn as a mule or perhaps acting bull-headed. Here's an example of something I witnessed first-hand the other day. A tow truck was getting ready to take a car for a tow. This was a flatbed style tow truck. You've surely seen these types of tow trucks on the roadways wherein they piggyback a car that needs to be transported. The tail end of the flatbed portion tilts at a somewhat acute angle to allow for driving a car up onto the riding platform. This forms a ramp for the car to traverse upward onto the empty and awaiting flatbed area.

As mentioned in the introduction, there are quite a few tools we need to import in our project to build our model. Although I've imported many libraries (Tensorflow, OpenCV, NumPy, etc.), the lines which I would like you to pay most attention to are the lines where I import layers and Sequential in addition to initializing the mnist variable. Layers is important as we will be using it to add hidden and output layers to our neural network (more on that in Creating Our Model). Importing Sequential allows us to later instantiate a normal feed-forward neural network. Lastly, the mnist variable allows us to access the thousands of images already stored to be used to train our model.

Genealogy site MyHeritage has unveiled a new AI tool that turns photos of deceased relatives into creepy videos. The DeepNostalgia feature is powered by tech developed by Israeli tech firm D-ID. Each driver is a video consisting of a fixed sequence of movements and gestures. Deep Nostalgia can very accurately apply the drivers to a face in your still photo, creating a short video that you can share with your friends and family. The driver guides the movements in the animation so you can see your ancestors smile, blink, and turn their heads. With our new Deep Nostalgia, you can see how a person from an old photo could have moved and looked if they were captured on video!

Heavily funded autonomous vehicle startup Aurora Innovation Inc. has acquired Ours Technology Inc., a fellow startup developing chip-based lidar sensors based on a new approach known as frequency-modulated continuous-wave sensing. The deal was announced today. Aurora is building an autonomous driving system that can be installed on trucks and cars to let them navigate the roads without human input. The startup has raised more than $1 billion in funding from investors including Amazon.com Inc. and Sequoia Capital. Aurora entered the headlines late last year when it bought Uber Technologies Inc.'s autonomous driving unit in a deal reportedly worth $4 billion.

The most impressive thing about OpenAI's natural language processing (NLP) model, GPT-3, is its sheer size. With more than 175 billion weighted connections between words known as parameters, the transformer encoder-decoder model blows its 1.5 billion parameter predecessor, GPT-2, out of the water. This has allowed the model to generate text that is surprisingly human-like after only being fed a few examples of the task you want it to do. Its release in 2020 dominated headlines, and people were scrambling to get on the waitlist to access its API hosted on OpenAI's cloud service. Now, months later, as more users have gained access to the API (myself included), interesting applications and use cases have been popping up every day.

Companies in several industries are building applications that take advantage of the connection between artificial intelligence and machine learning.