Key building blocks for applying artificial intelligence in enterprise applications are data analytics, data science and machine learning, including its deep learning subset. Data engineering also plays an important role. In the first article in this series, we discussed how humans have always desired to better understand the present and predict the future.1 The algorithms to help achieve this understanding have been around for decades, including even those of artificial intelligence (AI) approaches for enabling computers to reason about things that normally require human intelligence. However, only in recent years have we accumulated the massive digital data and developed the sufficiently powerful processors needed to put these AI algorithms to work on real human and business problems, with excellent performance and accuracy, on a broad scale.

In the absence of codified humanistic values within the big tech giants, personal experiences and ideals are driving decision-making. This is particularly dangerous when it comes to AI, because students, professors, researchers, employees, and managers are making millions of decisions every day, from seemingly insignificant (what database to use) to profound (who gets killed if an autonomous vehicle needs to crash). Artificial intelligence might be inspired by our human brains, but humans and AI make decisions and choices differently. Princeton professor Daniel Kahneman and Hebrew University of Jerusalem professor Amos Tversky spent years studying the human mind and how we make decisions, ultimately discovering that we have two systems of thinking: one that uses logic to analyze problems, and one that is automatic, fast, and nearly imperceptible to us. Kahneman describes this dual system in his award-winning book Thinking, Fast and Slow.

OpenAI, the non-profit organization that researches artificial intelligence, co-founded by Elon Musk in 2016, has been making big advancements -- even after Musk parted ways amid disagreements about its direction. Researchers have developed systems that can play games, write news articles, and move physical objects with groundbreaking levels of dexterity. OpenAI has caused controversy with its research. Last week, it announced the development of a language model, GTP2, that can generate texts with limited prompts. Given the human-written prompt "Miley Cyrus was caught shoplifting from Abercrombie and Fitch on Hollywood Boulevard today," the system produced a believable complete story that continued with "the 19-year-old singer was caught on camera being escorted out of the store by security guards."

Although artificial intelligence does not replace human marketing teams, it does support employees in controlling campaigns or even in content marketing. In the future, AI could increasingly displace a popular means of expressing branding: emotions. One change will be to localize marketing measures, even more, pushing to make the distribution of content production decentralized; accompanying this action by an organic reduction in personnel. Eventually, however, algorithms will replace only part of the marketing efforts. Although the news about AI has far less impact than previously thought, AI will dramatically change marketing.

Startups are powerful engineers of the Fourth Industrial Revolution, developing intelligent applications across industries. By leveraging the capabilities of robotics, hardware and high performance computing, startups are redrawing the maps of navigation, transportation, and supply-chain operations. NVIDIA is nurturing the entrepreneurship ecosystem through its Inception Program for AI Startups, connecting pioneers to a massive network of deep learning experts and thought leadership. Members of the program receive GPU hardware discounts and get the chance to try out the latest technology. At this year's GPU Technology Conference, the premier artificial intelligence and deep learning event, Inception members can take part in exclusive networking initiatives to share their emerging technologies.

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.

San Francisco (CNN Business)A new artificial intelligence system is so good at composing text that the researchers behind it said they won't release it for fear of how it could be misused. Created by nonprofit AI research company OpenAI (whose backers include Tesla CEO Elon Musk and Microsoft), the text-generating system can write page-long responses to prompts, mimicking everything from fantasy prose to fake celebrity news stories and homework assignments. It builds on an earlier text-generating system the company released last year. Researchers have used AI to generate text for decades with varying levels of success. In recent years, the technology has gotten particularly good.

We've come a long way in the word embedding space since the introduction of Word2Vec (Mikolov et. These days, it seems that every single machine learning practitioner can recite the "king minus man plus woman equals queen" mantra. In present, these interpretable word embeddings have become an essential part in many deep-learning based NLP systems. Earlier last October, Google AI introduced BERT: Bidirectional Encoder Representations from Transformers (paper, source). Seemingly, the researchers at Google have done it again: they've come up with a model to learn contextual word representations that redefined the state of the art for 11 NLP tasks, 'even surpassing human performance in the challenging area of question answering'.

Last year, InformationWeek published a high-level introduction to deep learning that was meant to explain the basics of the technology to CIOs and IT managers. Since then, interest in deep learning has skyrocketed, so now seems like a good time to revisit the topic with a deeper dive into the technology. Enterprises have been spending a lot of money on deep learning and related technologies -- and they are about to spend much more. According to IDC, spending on artificial intelligence (AI), which includes deep learning, will likely grow from an estimated $24.0 billion in 2018 to $77.6 billion in 2022. In other words, AI investments will triple in just four years.