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Tech leaders fear the outcomes of biased AI: report

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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.


The Future Of AI Process Automation In Marketing

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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.


AI marketing: How to leverage the innovative tech for e-commerce

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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.


ServiceNow BrandVoice: The Truth About Chatbots

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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.


What Is Fake News? & How Do Artificial Intelligence And Deep Fakes Work?

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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).


Machine Learning: Regularization Techniques

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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.


Getting AI from the lab to production

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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.


How robots learn to hike (w/video)

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To navigate difficult terrain, humans and animals quite automatically combine the visual perception of their environment with the proprioception of their legs and hands. This allows them to easily handle slippery or soft ground and move around with confidence, even when visibility is low.


Artificial Intelligence Intermediate Level Interview Questions

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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.


AI Week 2022

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We are thrilled to welcome data scientists and AI professionals to Israel's leading AI conference. Combining technological leadership, applied AI and cutting-edge research, AI Week will highlight the way in which AI technology is revolutionizing business strategy, policy and future development.