AutoML is still a novice concept, albeit an exciting one with rapid advances in Machine Learning and Deep Learning.

ST. LOUIS, MO – July 17, 2020 – World Wide Technology (WWT) today announced that it has been selected by the NVIDIA Partner Network (NPN) as the 2019 Deep Learning AI Partner of the Year for the Americas. This is the third year that WWT has been honored in this category. The NPN selected WWT for its ongoing AI research and development program. To help customers develop AI leadership, WWT published six white papers about leveraging the compute power of NVIDIA DGX systems to develop Machine Learning and Deep Learning models for real-time edge video analytics, network optimization, and performance comparisons of multiple reference architectures for ML model development. The WWT research into ML and Deep Learning is tied to real-world business outcomes, and improvements in mining safety, utilities grid optimization, and resource management for manufacturing.

A lot of ink has been spilled (or pixels illuminated) about the wonders of GPT-3, OpenAI's latest and greatest language model. A team of more than 30 OpenAI researchers have released a paper about GPT-3, a language model capable of achieving state-of-the-art results on a set of benchmark and unique natural language processing tasks that range from language translation to generating news articles to answering SAT questions. But like most examples spat out by language models, almost all of these were hand-selected by humans after many runs. Because not-so-good results just wouldn't make the news. Even bearing that in mind, I'm still blown away by what I've seen of GPT-3.

Did you know AI can now produce poetry and write fiction? That is not all it can also generate CODE ! Isn’t that amazing ! OpenAI’s newest AI language model – GPT-3, is trending all over the internet! Source: Unite.AI Background Elon Musk and Sam Altman started OpenAI in 2015 to advance the state of the art of AI and to ensure AI was used for the human good. OpenAI recently released the third version Generative Pre-training Transformer (GPT) – GPT-3. They first described GPT-3 through a research paper published in May but last week rolled out a beta version access to a select set of people and its capabilities are mind blowing! What is GPT-3 and why is everyone talking about it? Here are some quick pointers to help you understand what GPT-3 is, and why it is a step towards an AI revolution. Third version Generative Pre-training Transformer (GPT) A natural language generator (NLG) capable of producing human-like text on demand State-of-the-art language model made up of 175 billion parameters ~10X larger than Microsoft’s Turing NLG that has 17 billion parameters >100X larger than its own predecessor GPT-2 (released last year) trained on 1.5 billion parameters Does not require large custom, task specific datasets (which are usually difficult to get) Does not even require task specific model architectures What all can GPT-3 do for you? Here are a few things that GPT-3 can do for you and as more developers and experts experiment with it, more use cases will surface in the times to come. Answer questions with common sense (that doesn’t seem very difficult?) Write creative fiction – poetry, essays, stories Write news (Could that be a problem?) Solve arithmetic problems Generate Functioning Code (GPT-3 can CODE!) Design – developers demonstrated it with a Figma plugin (That’s truly creative!) This is just an illustrative list of what all GPT-3 can do! As more and more developers/experts experiment with the beta access, more innovations are bound to surface. Possible flaws and concerns pertaining to GPT-3? Although GPT-3 has proven brilliance in many ways in its current state but it also has certain flaws and concerns that it raises: Lacks an overarching, long-term sense of meaning and purpose As it generates its output word-by-word, based on the immediately surrounding text. It can struggle to maintain a coherent narrative or deliver a meaningful message over more than a few paragraphs is what experts say after initial experimentation. Possibility of being prone to certain biases Developers noticed that GPT-3 is prone to shoot out racist and sexist language, even when the prompt is something harmless. As GPT-3 is trained on internet scale data, these biases arise from biases in that training data reflecting possible societal views and opinions. Chances of misuse once released publicly Authors acknowledge that people can misuse it in several ways because of its ability to create text that is indistinguishable from that of written by humans. It can lead to creation of including generating misinformation and spam, phishing, and even fake academic essays. Possible impact on jobs As GPT-3 can perform human-like tasks across multiple domains, it could possibly have an impact on jobs that it can do like writers, coders, journalists, etc. These are just possible concerns and basis point 1 highlighted above many believe that instead of replacing humans, GPT-3 could become the perfect assistant for humans in these professions. Conclusion and what lies ahead? GPT-3 can execute plethora of NLP- based tasks, without fine-tuning for a specific task. Experts are even saying that this could be a step towards Artificial General Intelligence (AGI) – Read my article on Demystifying AI to know more! It is capable of performing machine translation, answering to questions, scripting poems and stories, elementary mathematics and can even generate code. OpenAI wants developers to help it explore what GPT-3 can do and with the beta release of its API has attracted plethora of experts and developers to experiment with the capabilities of GPT-3. It is only a matter of time before more exciting innovations surface in the developers’ community! Watch out for more interesting articles on AI! Feel free to share your thoughts. References [1] https://www.forbes.com/sites/robtoews/2020/07/19/gpt-3-is-amazingand-overhyped/#7c6166501b1c [2] https://analyticsindiamag.com/open-ai-gpt-3-code-generator-app-building/ [3] https://singularityhub.com/2020/06/18/openais-new-text-generator-writes-even-more-like-a-human/ [4] https://towardsdatascience.com/gpt-3-for-the-people-2cdd003d9a89 [5] https://www.technologyreview.com/2020/07/20/1005454/openai-machine-learning-language-generator-gpt-3-nlp/ [6] https://www.analyticssteps.com/blogs/what-openai-gpt-3 [7] https://www.cnbc.com/2020/07/23/openai-gpt3-explainer.html [8] https://analyticsindiamag.com/how-openais-gpt-3-can-be-alarming-for-the-society/ [9] https://www.forbes.com/sites/robtoews/2020/07/19/gpt-3-is-amazingand-overhyped/#7c6166501b1c [10] https://www.independent.co.uk/life-style/gadgets-and-tech/news/gpt3-ai-tool-designs-websites-medicine-a9627966.html [11] https://medium.com/fair-bytes/how-biased-is-gpt-3-5b2b91f1177 [12] https://towardsdatascience.com/gpt-3-demos-use-cases-implications-77f86e540dc1

The world has a new AI toy, and it's called GPT-3. The latest iteration of OpenAI's text generating model has left many starstruck by its abilities – although its hype may be too much. GPT-3 is a machine learning system that has been fed 45TB of text data, an unprecedented amount. All that training allows it to generate sorts of written content: stories, code, legal jargon, all based on just a few input words or sentences. And the beta test has already produced some jaw-dropping results.

In the previous episode, we discussed that intelligence has no single meaning, Computers can be intelligent as they can solve multiplications and divisions faster than you and me, but when it comes to making sense of world around us, it shits its pants. Whereas humans can do these tasks almost instantly and with little to no conscious thoughts and efforts. But they are catching up by mimicking the most intelligent thing known to us, i.e. And now they are calling this whole artificial neural networks thingy Artificial Intelligence. Artificial as in not natural, we humans are naturally intelligent, machines!

We decided to find out by asking the video game AI Dungeon, fueled by today's most highly advanced content creation platform, to co-write an article about … itself. As you can see from the resulting collaboration below, which includes our interview with the game's creator, the game has quite a lot to say. While the game offers players several traditional genre options--fantasy, sci-fi, cyberpunk, etc.--when starting a new campaign, we chose the "custom" experience and entered the text below to help the game's AI set the stage: You are an Adweek reporter writing about a text adventure role-playing game called AI Dungeon, which is powered by cutting-edge language generation machine learning. The software engine behind the game was recently upgraded to research group OpenAI's latest copy-generating model called GPT-3, trained on upwards of 100 times more data than the previous version, GPT-2. Released last month, the system was fed around a trillion words and cost $12 million to develop. The project has been controversial from day one.

The prevalence of machine learning has been increasing tremendously in recent years due to the high demand and advancements in technology. The potential of machine learning to create value out of data has made it appealing for businesses in many different industries. Most machine learning products are designed and implemented with off-the-shelf machine learning algorithms with some tuning and minor changes. In this post, I will cover the most common algorithms in the first two categories. Note: Although deep learning is a sub-field of machine learning, I will not include any deep learning algorithms in this post.

IBM Research, with the help of the University of Texas Austin and the University of Maryland, has tried to expedite the performance of neural networks by creating technology, called BlockDrop. Behind the design of this technology lies the objective and promise of speeding up convolutional neural network operations without any loss of fidelity, which can offer a great savings of cost to the ML community. This could "further enhance and expedite the application and use as well as boost the performance of neural nets, leading to particularly in places and on cloud/edge servers with limited computing capability and power limitations". An increase in accuracy level have been accompanied by increasingly complex and deep network architectures. This presents a problem for domains where fast inference is essential, particularly in delay-sensitive and realtime scenarios such as autonomous driving, robotic navigation, or user-interactive applications on mobile devices.

Deep learning-based marker detection for autonomous drone landing is widely studied, due to its superior detection performance. However, no study was reported to address non-uniform motion-blurred input images, and most of the previous handcrafted and deep learning-based methods failed to operate with these challenging inputs. To solve this problem, we propose a deep learning-based marker detection method for autonomous drone landing, by (1) introducing a two-phase framework of deblurring and object detection, by adopting a slimmed version of deblur generative adversarial network (DeblurGAN) model and a You only look once version 2 (YOLOv2) detector, respectively, and (2) considering the balance between the processing time and accuracy of the system. To this end, we propose a channel-pruning framework for slimming the DeblurGAN model called SlimDeblurGAN, without significant accuracy degradation. The experimental results on the two datasets showed that our proposed method exhibited higher performance and greater robustness than the previous methods, in both deburring and marker detection.