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) …
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.
Did you miss a session from the Future of Work Summit? The enterprise is eager to push AI out of the lab and into production environments, where it will hopefully usher in a new era of productivity and profitability. But this is not as easy as it seems because it turns out that AI tends to behave much differently in the test bed than it does in the real world. Getting over this hump between the lab and actual applications is quickly emerging as the next major objective in the race to deploy AI. Since intelligent technology requires a steady flow of reliable data to function properly, a controlled environment is not necessarily the proving ground that it is for traditional software.
The environment is the setting that the agent is acting on and the agent represents the RL algorithm. To understand this better, let's suppose that our agent is learning to play counterstrike. The mathematical approach for mapping a solution in Reinforcement Learning is called Markov's Decision Process (MDP). To briefly sum it up, the agent must take an action (A) to transition from the start state to the end state (S). While doing so, the agent receives rewards (R) for each action he takes.