September 23, 2019 -- Fujifilm SonoSite Inc. and the Allen Institute of Artificial Intelligence (AI2) Incubator, builder of AI-first startups, announced a …

Nowadays, the workplace is changing. Jobs are changing, some even becoming "obsolete." A large part of this flux is due to artificial intelligence being the new normal, increasing productivity and altering traditional job roles. More than ever, hiring managers have to look at what positions can be entirely filled with robots, and which can be assisted with the help of AI to produce more and cost less. This means that who is being hired is being impacted.

Artificial intelligence in marketing is part of a long list of traditional departments being shaken up by technology, in this case, AI. Technology is not only disrupting entire industries, but it is also changing the dynamics of departments. Think about how radical the change has been in advertising and marketing, portrayed in the 1960s by the hit TV show, Mad Men, compared to today, for example. The landscape has shifted from the physical to the digital and marketing, like many other disciplines, has had to adapt to this shift; reaching customers via different platforms with different means. The message has also changed and in today's world, personalisation is king.

The Defense Department today is wrapping up a three-day "hackathon" aimed at using artificial intelligence for aircraft maintenance.

Artificial intelligence is an area of innovation where regulation is necessary but can't be allowed to curtail innovation.

Businesses still don't have a clear understanding of what to expect when it comes to the ROI of AI.

Newswire) GBT Technologies Inc. (OTCPINK: GTCH) ("GBT", or the "Company"), a company specializing in the development of Internet of Things (IoT) and Artificial Intelligence (AI) enabled networking and tracking technologies, including its GopherInsight global mesh network technology platform for both mobile and fixed solutions, today announced that it commenced R&D activities for machine learning and analog computing, specifically targeting the area of robotics. GBT plans to further research analog computing to drive more improvements in computational efficiency. One of the main aims of this new research and development effort is to provide machine learning algorithms with better resilience to noise and uncertainty, while avoiding a trade-off between accuracy and numerical precision. Analog computing is an innovative emerging field which GBT intends to invest in, in order to enable its AI technology with analog features. GBT will base this new R&D effort on software to be integrated with its existing platforms and hardware.

TLDR: Depends how hard your problem is. In the strictest sense, you don't necessarily need formal training. I'm sure a smart enough person like Stephen Hawking (RIP) could figure pretty much anything out with enough years of diligent study with various online resources. However, for us mere mortals (no pun intended), it's hairier. AI is very vast, and depending on your case, you could need a lot of formal training or none at all.

We've trained a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization--all without task-specific training. Our model, called GPT-2 (a successor to GPT), was trained simply to predict the next word in 40GB of Internet text. Due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper. GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data. GPT-2 displays a broad set of capabilities, including the ability to generate conditional synthetic text samples of unprecedented quality, where we prime the model with an input and have it generate a lengthy continuation. In addition, GPT-2 outperforms other language models trained on specific domains (like Wikipedia, news, or books) without needing to use these domain-specific training datasets. On language tasks like question answering, reading comprehension, summarization, and translation, GPT-2 begins to learn these tasks from the raw text, using no task-specific training data.