Since the enactment of the North American Free Trade Agreement (NAFTA), Mexican customs authorities have had the ability to conduct verifications to confirm the NAFTA origin of goods imported into Mexico (NAFTA origin audits). As the years have passed, however, verifications have become more frequent and sophisticated as to the information and documents authorities expect to receive as evidence to maintain preferential admission into the country.
As cities prepare for not only the return of every park-visitor’s favorite seasonal bird, the menace known as the Canadian Goose, cities are also bracing for the seasonal arrival of scooter sharing services like Lime, Bird, Spin and their competitors along their curbs and on street corners throughout urban cores. Unlike the Canadian Goose, which does what it wants, when it wants, to whomever it wants to, cities are looking to not only reign in usage and set rules for operations, but also work with scooter sharing services as they struggle to tackle the Last Mile Issue plaguing cities around the world. A major component of this initiative includes maximizing the potential revenue cities and scooter companies can generate in an already congested urban fabric through fees and optimized usage patterns. We discussed the Last Mile Issue in our May 2018 blog post, Ride Sharing and Cities Team Up on Transit, Last Mile Issues. Unlike our recent post on Congestion Pricing, cities and scooter companies are looking to use data not to necessarily discourage usage in certain areas, but rather identify usage patterns and optimize placement in a way to be both effective for consumers and commuters, alike.
The transition from traditional manufacturing techniques and technologies to techniques leveraging automation and data exchange technologies, cyber-physical systems, the Internet of things, cloud computing and cognitive computing, sometimes referred to as “Industry 4.0,” holds great promise for manufacturers but, like any change, also holds dangers for the unwary. Continue reading this entry
Anyone following electric vehicle news in the past several months may feel like they are watching a yo-yo: first one analyst will predict an EV boom, and then another analyst will predict an EV sales slowdown. However, despite the differences of opinion on short term sales, signs continue to point to long term sustained growth in EVs sales.
Machine Learning. Deep Learning. Data Mining. Predictive Analytics. Natural Language Processing.
These are the buzzwords used to describe the pivotal artificial intelligence (AI) space. Companies in every industry, from automotive and electronics to financial services, health care and life sciences, are working to deploy these advanced technology methods in order to bring their innovations to the next level. AI can help pathologists identify diseases, and physicians better assess brain health. It can help bankers automate back-office processes, create more lifelike chatbots, and improve fair lending practices. It can process and collect data more efficiently, protect from cyberattacks, and improve driver safety. As with any disruptive technology, however, this AI race to the moon comes with its share of risks and challenges. Are you prepared to address the various issues that this new technology may bring?
That is just the tip of the iceberg. As one security professional put it: “For large countries, growing and investing in AI is now a matter of national security and longevity. It’s the next natural resource.” Developing AI safely, legally, and efficiently is an uphill battle that — if navigated incorrectly — could result in a disappointing, if not outright dangerous, assortment of missed opportunities, according to Foley & Lardner LLP’s AI Report, which features qualitative research and conversations with startup founders, business executives, and attorneys at Foley working with AI on:
- The Dangers of Hype
- Access to Quality Data
- An Uncertain Regulatory Landscape
- The Intellectual Property Conundrum
- More Data, More Privacy Concerns
- The Double-Edged Sword of Cybersecurity
- The Talent Gap
At the end of the day, AI, like all technology, is resolutely human. But that doesn’t mean it can’t improve society. If we seize the AI opportunity thoughtfully — with humanity, ethics, education, testing, and due diligence across organizations and functionalities — perhaps we can, as Michael Campos, research scientist and director of IP at NetraDyne Inc., suggests, “make systems that are a little better than we are.”
To access Foley’s full AI Report, please click here and follow the instructions provided.