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) …
IBM launched a series of tools that revolve around accelerating AI adoption in the enterprise including one called Watson Orchestrate that may be set up to be a knowledge worker's digital twin. CEO Arvind Krishna's overarching message at IBM's Think conference is that the company is all in on AI and hybrid cloud. The company showed traction in those categories in the first quarter and is structuring itself on those two areas. Indeed, the build up to Think has been busy. "In the same way that we have electrified factories and machines in the past century, we will infuse AI into software and systems in the 21st century," said Krishna.
Gartner predicts that "by 2022, 70 percent of white-collar workers will interact with conversational platforms on a daily basis." As a result, the research group found that more organizations are investing in chatbot development and deployment. IBM Business Partners like Sopra Steria are making chatbot and virtual assistant technology available to businesses. Sopra Steria, a European leader in digital transformation, has developed an intelligent virtual assistant for organizations across several industries who want to use an AI conversational interface to answer recurrent customer service questions. In developing our solution, we at Sopra Steria were looking for AI technology that was easy to configure and could support multiple languages and complex dialogs.
MLOps, a compound of "machine learning" and "information technology operations," is a newer discipline involving collaboration between data scientists and IT professionals with the aim of productizing machine learning algorithms. The market for such solutions could grow from a nascent $350 million to $4 billion by 2025, according to Cognilytica. But certain nuances can make implementing MLOps a challenge. A survey by NewVantage Partners found that only 15% of leading enterprises have deployed AI capabilities into production at any scale. Still, the business value of MLOps can't be ignored.
Successful businesses not only have great products and services; they also have a deep understanding of their customers. Companies that can use behavioral analytics in marketing automation platforms are better equipped to deliver real-time marketing efforts. According to a research case study from Deloitte, companies with a customer-centric business model are 60% more profitable. Knowing and adapting this differentiating business model is the key to becoming a market leader in today's fierce competitive landscape. With a 3.8 billion user base and 86% of the users interacting daily, social media has become an impactful customer voice through platforms like Twitter, Facebook, and Reddit.
In 2019, the U.S. Postal Service had a need to identify and track items in its torrent of more than 100 million pieces of daily mail. A USPS AI architect had an idea. Ryan Simpson wanted to expand an image analysis system a postal team was developing into something much broader that could tackle this needle-in-a-haystack problem. With edge AI servers strategically located at its processing centers, he believed USPS could analyze the billions of images each center generated. The resulting insights, expressed in a few key data points, could be shared quickly over the network.
The US Postal Service runs a massive operation, processing 129 billion pieces of mail a year, including 7.3 billion packages. So when a package gets lost, there's a lot of sorting involved in finding it. What is AI? Everything you need to know about Artificial Intelligence With a new, Nvidia-powered AI program, the USPS has built a way to dramatically reduce the time it takes to find lost packages, down from several days to just two hours. Package sorting is just the beginning -- the USPS now has ideas for dozens of applications it could power with its new edge AI deployment, spanning everything from mail sorting to marketing. "There are not many enterprise-wide AI/ML projects that have been deployed at this scale across the whole enterprise, especially not in the case of government," Anthony Robbins, VP of Nvidia's federal government business, told reporters this week.
The European Automobile Manufacturers' Association (ACEA) welcomes the European Commission's initiative on Artificial Intelligence and its goal to develop a European ecosystem of excellence and trust around AI. ACEA supports the risk-based approach laid down in the proposal. Indeed, a key element for a successful European approach to AI is that the requirements set out in the Regulation are proportionate to the risk level of the AI applications, and are not too burdensome for businesses across Europe, as this would restrain innovation and hinder AI adoption. A coherent legal framework is crucial for accelerating AI deployment in motor vehicles. We stress our support to the sectoral approach taken by the Commission, as this will ensure that automotive products remain regulated primarily through their sector-specific framework. In order to avoid duplicating the existing governance mechanisms, ex ante conformity assessment procedures, and the monitoring and market surveillance in place for motor vehicles and their safety components, it is essential that the technical requirements for automotive products are integrated into the existing vehicle type-approval framework.
NUREMBERG, Germany and SUNNYVALE, CA, USA, May 5, 2021 – Google Cloud and Siemens, an innovation and technology leader in industrial automation and software, today announced a new cooperation to optimize factory processes and improve productivity on the shop floor. Siemens intends to integrate Google Cloud's leading data cloud and artificial intelligence/machine learning (AI/ML) technologies with its factory automation solutions to help manufacturers innovate for the future. Siemens and Google Cloud to cooperate to transform manufacturing by enabling scaled deployment of artificial intelligence. Data drives today's industrial processes, but many manufacturers continue to use legacy software and multiple systems to analyze plant information, which is resource-intensive and requires frequent manual updates to ensure accuracy. In addition, while AI projects have been deployed by many companies in "islands" across the plant floor, manufacturers have struggled to implement AI at scale across their global operations.
NI has bought autonomous vehicle simulator monoDrive, expanding the company's footprint into the autonomous driving space as the industry works to expand beyond testing into real-life situations. MonoDrive specializes in creating simulation software for advanced driver-assistance systems and autonomous vehicle development, helping train autonomous systems for their eventual introduction to roads with drivers and pedestrians. NI, formerly known as National Instruments, said the acquisition of monoDrive will help speed up the development, test, and deployment of safer autonomous systems for their transportation customers. According to NI, acquiring monoDrive will allow the company to "streamline the transitions between simulation, lab-based and physical test environments," noting that right now, "disparate tools cause siloed processes, time-to-market delays, and lead to higher costs that reduce the pace of innovation and hinder the quality of advanced technologies." "We welcome the monoDrive employees to NI and look forward to collectively accelerating our growth ambitions for our transportation business," said Chad Chesney, NI vice president and general manager of the transportation business unit.