There are stories across business-focused media about how companies should be leveraging the power of artificial intelligence and machine learning to streamline operations, improve customer service, boost marketing campaigns and more. Smaller businesses may well want to get in on the action and tap into the capabilities of AI/ML, but their leaders may think it's simply too expensive and, therefore, out of reach. Even if a small business can't make instantaneous, sweeping changes through AI/ML, it may still be the right time to take the first steps on the journey of building a strategy. Or, there may be AI/ML tools already in the marketplace that can help a small business make targeted, but meaningful, improvements. Below, 16 members of Forbes Technology Council share a variety of tips for small businesses interested in leveraging the power of AI/ML, from the best ways to get started to recommendations for the functions they might want to consider improving first.

Let's explore highlights and accomplishments of vast Google Machine Learning communities over the first quarter of the year! We are enthusiastic and grateful about all the activities that the communities across the globe do. ML Olympiad is an associated Kaggle Community Competitions hosted by Machine Learning Google Developers Experts (ML GDEs) or TensorFlow User Groups (TFUGs) sponsored by Google. The first round was hosted from January to March, suggesting solving critical problems of our time. Competition highlights include Autism Prediction Challenge, Arabic_Poems, Hausa Sentiment Analysis, Quality Education, Good Health and Well Being.

The event attracted more than 700 attendees and provided insights into how advanced technologies can help Customs administrations facilitate the flow of goods across borders. The publication titled, "The role of advanced technologies in cross-border trade: A customs perspective" provides the current state of play and sheds light on the opportunities and challenges Customs face when deploying these technologies. The publication outlines the key findings of WCO's 2021 Annual Consolidated Survey and its results on Customs' use of advanced technologies such as blockchain, the internet of things, data analytics and artificial intelligence to facilitate trade and enhance safety, security and fair revenue collection. The joint publication highlights the benefits that can result from the adoption of these advanced technologies, such as enhanced transparency of procedures, sharing of information amongst all relevant stakeholders in real time, better risk management, and improved data quality, leading to greater efficiency in Customs processes and procedures. In his remarks, WCO Deputy Secretary General Ricardo Treviño Chapa said, "Technologies will assist implementation of international trade facilitation rules and standards, such as the WCO Revised Kyoto Convention and the WTO Trade Facilitation Agreement. We are therefore delighted to be partnering with the WTO, to ensure that our work in assisting our Members' digital transformation journeys is complementary, that we bring all relevant partners to the same table, and that we avoid duplication."

Analytics has been changing the bottom line for businesses for quite some time. Now that more companies are mastering their use of analytics, they are delving deeper into their data to increase efficiency, gain a greater competitive advantage, and boost their bottom lines even more. That's why companies are looking to implement machine learning (ML) and artificial intelligence (AI); they want a more comprehensive analytics strategy to achieve these business goals. Learning how to incorporate modern machine learning techniques into their data infrastructure is the first step. For this many are looking to companies that already have begun the implementation process successfully. For call centers, using ML and AI means having conversation analytics software in place – in fact, decades ago call centers began using primitive forms of artificial intelligence.

Tensor PCA is a stylized statistical inference problem introduced by Montanari and Richard to study the computational difficulty of estimating an unknown parameter from higher-order moment tensors. Unlike its matrix counterpart, Tensor PCA exhibits a statistical-computational gap, i.e., a sample size regime where the problem is information-theoretically solvable but conjectured to be computationally hard. This paper derives computational lower bounds on the run-time of memory bounded algorithms for Tensor PCA using communication complexity. These lower bounds specify a trade-off among the number of passes through the data sample, the sample size, and the memory required by any algorithm that successfully solves Tensor PCA. While the lower bounds do not rule out polynomial-time algorithms, they do imply that many commonly-used algorithms, such as gradient descent and power method, must have a higher iteration count when the sample size is not large enough. Similar lower bounds are obtained for Non-Gaussian Component Analysis, a family of statistical estimation problems in which low-order moment tensors carry no information about the unknown parameter. Finally, stronger lower bounds are obtained for an asymmetric variant of Tensor PCA and related statistical estimation problems. These results explain why many estimators for these problems use a memory state that is significantly larger than the effective dimensionality of the parameter of interest.

Artificial Intelligence (AI) is much more than just a buzzword nowadays. It powers facial recognition in smartphones and computers, translation between foreign languages, systems which filter spam emails and identify toxic content on social media, and can even detect cancerous tumours. These examples, along with countless other existing and emerging applications of AI, help make people's daily lives easier, especially in the developed world. As of October 2021, 44 countries were reported to have their own national AI strategic plans, showing their willingness to forge ahead in the global AI race. These include emerging economies like China and India, which are leading the way in building national AI plans within the developing world.

In a proliferated, multi-channel world, every brand needs to win the heart and mind of the consumer to acquire and retain them. They need to set up a foundation of empathy and connectedness. Artificial intelligence combined with a human-centric approach to marketing might seem like a contrarian model. But the truth is that machine learning, AI and automation are vital for brands today to transform data into empathetic, customer-centric experiences. For marketers, AI-based solutions serve as a scalable and customizable tool capable of understanding the motive behind consumer interactions.

Artificial Intelligence (AI) is much more than just a buzzword nowadays. It powers facial recognition in smartphones and computers, translation between foreign languages, systems which filter spam emails and identify toxic content on social media, and can even detect cancerous tumours. These examples, along with countless other existing and emerging applications of AI, help make people's daily lives easier, especially in the developed world. As of October 2021, 44 countries were reported to have their own national AI strategic plans, showing their willingness to forge ahead in the global AI race. These include emerging economies like China and India, which are leading the way in building national AI plans within the developing world.

Lilt, a provider of AI-powered business translation software, today announced that it raised $55 million in a Series C round led by Four Rivers, joined by new investors Sorenson Capital, CLEAR Ventures and Wipro Ventures. The company says that it plans to use the capital to expand its R&D efforts as well as its customer footprint and engineering teams. "Lilt [aims to] build a solution that [will] combine the best of human ingenuity with machine efficiency," CEO Spence Green told TechCrunch via email. "This new funding will … [reduce our] unit economics [to make] translation more affordable for all businesses. It will also [enable us to add] a sales team to our existing production team in Asia. We are in three regions -- the U.S., Europe, the Middle East and Africa (EMEA) and Asia -- and look to have both sales and production teams in each of these regions."

JOHANN JUNGWIRTH, vice president of mobility-as-a-service at Intel-owned company Mobileye, says he spends two to three hours per day on the road. It's a long commute, especially given he's sitting behind the driver's wheel--except for the fact that he's not the one making decisions on the road. "I just push the Go button, and then, you know, I let it drive itself," Jungwirth tells WIRED after a Mobileye robotaxi drove him from Jerusalem to Tel Aviv. Driving enthusiasts can now check out what that experience looks like, thanks to a 45-minute long unedited video of Mobileye's seven-seater electric van ferrying ride-hailing passengers around Jerusalem's narrow, winding roads. As the robotaxi, which comes equipped with Mobileye's True Redundancy sensing system, drives to different drop-off and pick-up points, it easily dodges the jaywalkers, gives way to cars suddenly interrupting its route, and navigates around parked cars and other obstacles blocking the way.