If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Axiomtek has released the AIE500-901-FL, an advanced artificial intelligence (AI) embedded system for edge AI computing and deep learning applications. The device supports two CAN or two COM interfaces. The embedded system employs an Nvidia Jetson TX2 module which has a 64-bit ARM A57 processor; Nvidia Pascal GPU with 256 CUDA cores; and 8 GiB of 128-bit LPDDR4 memory. To withstand the rigors of day-to-day operation, the product has an operating temperature range of -30 C to 60 C and vibration of up to 3 Grms with its construction. According to the company, this fanless AI edge system is dedicated to achieving smart manufacturing and intelligent edge applications.
Earlier this year Tesla CEO Elon Musk said the future is now. By the middle of 2020, he said at an event for investors, Tesla's autonomous system will have improved to the point where drivers will not have to pay attention to the road. He revealed that Tesla has plans to roll out Level 5 autonomous taxis next year in some parts of the United States, which means they will be capable of driving themselves anywhere on the planet, under all possible conditions, with no limitations. That's compelling, but is it really possible within such a short timeframe? In May, a month after Musk's speech, Consumer Reports said that the new lane-changing feature on Tesla's updated Navigate on Autopilot software lags far behind a human driver's skills.
This article is the first installment of a two-post series on Building a machine reading comprehension system using the latest advances in deep learning for NLP. Stay tuned for the second part, where we'll introduce a pre-trained model called BERT that will take your NLP projects to the next level! In the recent past, if you specialized in natural language processing (NLP), there may have been times when you felt a little jealous of your colleagues working in computer vision. It seemed as if they had all the fun: the annual ImageNet classification challenge, Neural Style Transfer, Generative Adversarial Networks, to name a few. At last, the dry spell is over, and the NLP revolution is well underway!
Doing research to see where we currently are with faking voice audio with neural networks/deep learning. Learning to create voices from YouTube clips, and trying to see how quickly we can do new voices. In this case, I've used a Deep Convolutional Text to Speech (DCTTS) model to produce pretty darn good results. The voices in the first 2 minutes are all fake. Real Computer Vision for mobile and embedded https://morioh.com/p/974fc441c295/
Natural Language Generation (NLG) is a well studied subject among the NLP community. With the rise of deep learning methods, NLG has become better and better. Recently, OpenAI has pushed the limits, with the release of GPT-2 -- a Transformers based model that predicts the next token at each time space. Nowadays it's quite easy to use these models -- you don't need to implement the code yourself, or train the models using expensive resources. HuggingFace, for instance, has released an API that eases the access to the pretrained GPT-2 OpenAI has published.
The reality of human-level artificial intelligence is still a dream. Even with all of the recent advancements in state-of-the-art AI, its ability to understand the world around us is only at the level of a one-year-old child. We don't yet know how to build a robot to match a two-year-old's ability to empathize or her ability to define new goals to help others. The software industry has entered a race to apply AI in every vertical possible. While AI technology adoption is accelerating, our ability to understand its potential impact isn't keeping up.
In the history of the quest for human-level artificial intelligence, a number of rival paradigms have vied for supremacy. Symbolic artificial intelligence was dominant for much of the 20th century, but currently a connectionist paradigm is in the ascendant, namely machine learning with deep neural networks. However, both paradigms have strengths and weaknesses, and a significant challenge for the field today is to effect a reconciliation. A central tenet of the symbolic paradigm is that intelligence results from the manipulation of abstract compositional representations whose elements stand for objects and relations. If this is correct, then a key objective for deep learning is to develop architectures capable of discovering objects and relations in raw data, and learning how to represent them in ways that are useful for downstream processing.
A mere four years ago AI was not even able to pass a Grade eight science test. Seven hundred computer scientists competed in a contest with a significant amount of money as prize. They had to build artificial intelligence that could pass a Grade eight science test. The computer scientists did their best, but not even the most advanced AI system could score better than 60percent in the test. It seems that the AI was just not advanced enough to fully reach the language and logic skills expected of students in the eighth grade.
Generally, in dynamic spectrum access (DSA) networks, co-operations and centralized control are unavailable and DSA users have to carry out wireless transmissions individually. DSA users have to know other users' behaviors by sensing and analyzing wireless environments, so that DSA users can adjust their parameters properly and carry out effective wireless transmissions. In this thesis, machine learning and deep learning technologies are leveraged in DSA network to enable appropriate and intelligent spectrum managements, including both spectrum access and power allocations. Accordingly, a novel spectrum management framework utilizing deep reinforcement learning is proposed, in which deep reinforcement learning is employed to accurately learn wireless environments and generate optimal spectrum management strategies to adapt to the variations of wireless environments. Due to the model-free nature of reinforcement learning, DSA users only need to directly interact with environments to obtain optimal strategies rather than relying on accurate channel estimations.