OpenAI's impressive AI language model GPT-3 has plenty of things going it, but with 175 billion parameters no one would claim it's particularly streamlined. The Allen Institute for AI (AI2) has demonstrated a model that performs as well or better than GPT-3 on answering questions, but is a tenth the size. Macaw, AI2's model, emerged from research being done at the nonprofit into creating an AI that performs at human levels on standardized tests. "After we got a very high score they moved on to harder questions," said AI2 head Oren Etzioni. "There's this paradox where sometimes the questions that are easiest for people are the hardest for machines -- and the biggest gap was in common sense."

Think about a cigarette that knows its owner, refuses to be smoked in public, and alarms the parents if it identifies that their children are smoking. How about cigarettes that identify the health of a person, his behaviour and alarm them to not smoke when it is dangerous. This is the power of AI in smoking. Let us explore some of the possibilities that might become realities in the future of smoking. As of the year 2019, there were more than 1 billion smokers in the world and I am sure the number would have increased today.

AI can be a powerful tool for law enforcement and help in addressing many types of crimes. It can help law enforcement to optimize their resources in specific areas and at specific times, to cover as much ground as possible with the same or even fewer resources. Drones with sensors, for instance, can also be used to detect illegal movements such as illegal border crossings, human traffickers, and vessels illegally fishing. Location is a powerful piece of information for AI systems. In India too, artificial intelligence tools are increasingly being put to use.

Big Tech has long faced accusations that it's a detriment to society, and Google thinks it can address those criticisms more directly. Axios' Ina Fried says the internet pioneer has hired James Manyika as the company's first Senior VP of Technology and Society. As Google told Engadget, the McKinsey Global Institute director will help explore tech's impact on society and shape the firm's points of view on subjects including AI, the future of work, sustainability and other areas that could make a significant difference. Manyika will report directly to Alphabet and Google chief Sundar Pichai, and will work with outsiders as well as internal staff. He'll help build leadership on technological impact at the company, Google said, and will focus on top-level, longer-term initiatives.

Companies working with AI fear losing money or staff over AI bias, but there's additional risk in being outpaced by competition if projects fail due to AI bias. To jump ahead of algorithmic bias, over half of companies with mature AI implementations check the fairness, bias and ethics of their AI platforms, according to the O'Reilly 2021 AI Adoption in the Enterprise report. One approach yielding results for organizations is the development of in-house centers of excellence, said Marshall Choy, SVP, product at SambaNova. These institutions can address the technical aspects of AI as well as "the business and organizational implications of governance, dealing with topics like bias and ethics of AI." Despite ethical challenges, AI remains a top enterprise technology priority.

In the past several years, marketers have embraced artificial intelligence technologies to automate a broad range of high-volume, data-intensive tasks from ad targeting to image manipulation. The next phase of AI in marketing has the potential to deliver a much larger impact as the focus shifts from the automation of single tasks to more complex business processes and workflows, and ultimately influencing marketing strategy. Task automation using AI will continue to add value to marketers, but their benefits will be dwarfed by the intelligent automation of complex workflows. To understand the enormous difference between task automation and process automation, consider the evolution of automotive interfaces. In the early 2000s, we started to see basic voice automation in cars.

When you think of artificial intelligence (AI) your mind is naturally drawn to Skynet or Blade Runner. Evolved, sentient beings, often with a desire to rise up against humanity for some reason. While we're not quite there (yet) AI technology is certainly on the rise. Especially when it comes to AI marketing. AI has substantial benefits and applications in marketing in fact -- so it's time e-commerce companies got on board to leverage this transformative technology.

Love them or hate them, chatbots are here to stay. According to Mordor Intelligence, the chatbot market will grow over 34% between 2021 and 2026, when the market is expected to reach $102 billion. Driven by advances in natural language understanding (NLU), chatbots have become a staple of digital transformation in customer service, healthcare, and financial services by providing intelligent interactions between people and a digital interface. Today, many organizations are embracing internal chatbots as a way to improve the employee experience and reduce costs. While this sounds relatively straightforward, there's a difference between implementing an internal and external chatbot.

Have you heard about fake news, AI, and deep fake? This blog post is going to introduce you to all three of these terms, giving a broad overview of just what is going on. Are we living in a science fiction film? Are computers going to bring about the end of civilization as we know it? This isn't fiction, so I'm not talking about Brave New World (although you should read that book).

A sufficiently complex neural network can result in has 100% accuracy on the data it was trained with, but significant error on any new data. When this occurs, the network is likely overfitting the training data. This means that it makes predictions that are too strongly attached to features it learned in training, but which don't necessarily correlate with the expected results. One way to temper overfitting is by using a process called regularization. Regularization generally works by penalizing a neural network for complexity.