The seed for this article was planted when Anant was struck by a headline on his Twitter feed: "You don't need ML/AI. He had observed something similar in working through data and analytics requirements for Google Cloud's Apigee team -- not that machine learning (ML) or artificial intelligence (AI) is not needed, but that good database queries can frequently accomplish the job, and that when AI is legitimately needed, its role is often to improve the database design and operations, not to replace them. The two of us got the chance to compile our thinking a bit more as Anant was preparing for a talk at VLDB 2018, a premier database conference. The slides of his talk are here. In this post, we elaborate on some of our observations on the topic.
Since before the dawn of the computer age, scientists have been captivated by the idea of creating machines that could behave like humans. But only in the last decade has technology enabled some forms of artificial intelligence (AI) to become a reality. Interest in putting AI to work has skyrocketed, with burgeoning array of AI use cases. Many surveys have found upwards of 90 percent of enterprises are either already using AI in their operations today or plan to in the near future. Eager to capitalize on this trend, software vendors – both established AI companies and AI startups – have rushed to bring AI capabilities to market.
The most important aspect of a successful business is the ability to provide a customer experience that exceeds expectations. According to a GrooveHQ Blog Post on customer experience, 86% of customers are willing to pay more for better customer experience. These same customers have a 60%- 70% probability of recommending new customers to your business if they have a great customer experience. As a customer-centric organization, how do you know what makes the best experience for your customers? How do you know what works?
Underline the presenter's name - Affiliation: Provide authors' institutions and addresses. In the case of multiple institutions, identify them by roman numbers - The main body of the abstract should be structured with the following sub-headings: Background, Aim, Methodology, Findings, and Conclusion. It's important to also include the corresponding author's contacts: e-mail and telephone number.
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Facebook is certainly a high-tech company, but it's not one you would necessarily associate with robots. However, as the firm revealed today, that's exactly where its researchers are looking next -- trying to see how experiments in robotics can further its work in AI. A lot of firms, including Google, Nvidia, and Amazon, use robots as a platform to explore avenues of AI research. Controlling robots is, in many ways, trickier than challenges like playing board games and video games. With these latter tasks, researchers have access to simulated game environments, which allows AI agents to play and learn at accelerated speeds.
Facebook isn't often thought of as a robotics company, but new work being done in the social media giant's skunkworks AI lab is trying to prove otherwise. The company on Monday gave a detailed look into some of the projects being undertaken by its AI researchers at its Menlo Park, California-based headquarters, many of which are aimed at making robots smarter. Among the machines being developed are walking hexapods that resemble a spider, a robotic arm and a human-like hand complete with sensors to help it touch. Facebook has a dedicated team of AI researchers at its headquarters in Menlo Park, California that are tasked with testing out robots. The hope is that their learnings can be applied to other AI software in the company and make those systems smarter.
Facebook is trying to develop artificial intelligence models that will allow robots–including walking hexapods, articulated arms, and robotic hands fitted with tactile sensors–to learn by themselves, and to keep getting smarter as they encounter more and more tasks and situations. In the case of the spider-like hexapod ("Daisy") I saw walking around a patio at Facebook last week, the researchers give a goal to the robot and task the model with figuring out by trial and error how to get there. The goal can be as simple as just moving forward. In order to walk, the spider has to know a lot about its balance, location, and orientation in space. It gathers this information through the sensors on its legs.
Facebook has publicly spoken about its interest in robotics in the past, but on Monday, the company finally shared details regarding the specific projects it's working on. The social media giant unveiled three robotics projects that it hopes will contribute to solving the ongoing challenge of building artificial intelligence systems that don't have to rely on large quantities of labeled data to learn new information. To do so, the company is conducting research aimed at teaching robots how to learn about the world, similar to the way that humans do. "The real world is messy, it's difficult," Roberto Calandra, a research scientist in Facebook's AI division said when speaking to Business Insider. "The world is not a perfect place; it's not neat. So the fact that we are trying to develop algorithms that work on real robots [will] help to create [AI] algorithms that, generally speaking, are going to be more reliable, more robust, and that are going to learn faster."
This ebook, based on the latest ZDNet / TechRepublic special feature, advises CXOs on how to approach AI and ML initiatives, figure out where the data science team fits in, and what algorithms to buy versus build. It depends who you ask. Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. AI systems will typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity. AI is ubiquitous today, used to recommend what you should buy next online, to understand what you say to virtual assistants such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud. AI might be a hot topic but you'll still need to justify those projects.