Well-known Belfast startup Axial3D produces 3D prints of your body parts. This isn't to satisfy the narcissistic social media types – it has important surgical implications. This previous TechWatch article describes the company's process. Now, Axial3D is developing new AI techniques to make instantaneous the transition from 2D images to 3D prints. How are they doing that?
Kubernetes is becoming synonymous with cloud-native computing. As an open-source platform, it enables development, deployment, orchestration and management of containerized microservices across multicloud ecosystems. Kubernetes is the key to cloud-native microservices that are platform agnostic, dynamically managed, loosely coupled, distributed, isolated, efficient, and scalable. The maturation of Kubernetes continues to deepen as it leverages containers, orchestrations, service meshes, immutable infrastructure, and declarative APIs. One clear indicator of Kubernetes' maturation is the rich ecosystem of other open-source projects that have grown up around it.
Then there are those companies that view AI as a transformational platform that has the potential to create a new species of business. These future-minded companies are centering on the customer and using AI to connect people and processes and predict business outcomes, capitalizing on new revenue opportunities by applying new forms of intelligence. With research showing that half of the S&P 500 will be replaced over the next 10 years, organizations that view AI as merely a trend or a quick antidote to a business problem risk falling to the back of the pack or vanishing altogether. Meanwhile, businesses that are successfully building for the future realize that the actions they take today will permit their enterprises to sense and predict with accuracy so they can make better, faster decisions that transform the customer experience. In the current environment, not all businesses are created equal, says Sanjay Srivastava, chief digital officer at Genpact: "Firms that embed AI as their central nervous system will unlock a significantly more dynamic and powerful tomorrow and will act more like living organisms--enabling them to operate instinctively.
Businesses across the world are rapidly leveraging the Internet-of-Things (IoT) to create new products and services that are opening up new business opportunities and creating new business models. The resulting transformation is ushering in a new era of how companies run their operations and engage with customers. However, tapping into the IoT is only part of the story. For companies to realize the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (AI) technologies, which enable'smart machines' to simulate intelligent behavior and make well-informed decisions with little or no human intervention. Artificial Intelligence (AI) and the Internet of Things (IoT) are terms that project futuristic, sci-fi, imagery; both have been identified as drivers of business disruption in 2017.
The digital revolution has brought with it a new way of thinking about manufacturing and operations. Emerging challenges associated with logistics and energy costs are influencing global production and associated distribution decisions. Significant advances in technology, including big data and analytics, AI, Internet of Things, robotics and additive manufacturing, are shifting the capabilities and value proposition of global manufacturing. In response, manufacturing and operations require a digital renovation: the value chain must be redesigned and retooled and the workforce retrained. Total delivered cost must be analyzed to determine the best places to locate sources of supply, manufacturing and assembly operations around the world.
Machine learning is beginning to make a large impact in catalysis research, according to Bryan Goldsmith, Jacques Esterhuizen, and Jin-Xun Liu of the Univ. of Michigan, Christopher Bartel of the Univ. of Colorado Boulder, and Christopher Sutton of the Fritz Haber Institute of the Max Planck Society in their July AIChE Journal Perspective article, "Machine Learning for Heterogeneous Catalyst Design and Discovery." Novel catalysts are crucial for several applications, such as energy generation and storage, sustainable chemical production, and pollution mitigation. The current trial-and-error approaches to new catalyst discovery and synthesis are expensive and time-consuming. As an alternative, machine learning can be used to identify the top catalyst candidates before experimental testing, thereby accelerating catalyst discovery and design. Goldsmith and colleagues highlight several examples where machine learning is making an impact on heterogeneous catalysis research, such as: accelerating the determination of catalyst active sites and catalyst screening; finding descriptors and patterns in catalysis data; determining interatomic potentials for catalyst simulation; and discovering and analyzing catalytic mechanisms.
Artificial intelligence (AI) today is the new frontier in the digital transformation journey enterprises have already embarked on. But adoption to solve real problems and drive business outcomes has been slow. Driving up adoption is critical to unlock the real promise of AI and is going to depend on how we approach AI. And that opportunity is in front of us thanks to industry-optimized augmented intelligence. Most realistic and successful AI initiatives have been focused on augmenting human abilities with powerful machine intelligence.
Digital transformation is no longer an "if" but a "when" for enterprises across both public and private sector. The promise of greater efficiency, customer-centric products and services, rapid response to changing regulatory or economic requirements – and the chance to compete with disruptive start-ups percolating every sector of the economy can no longer be overlooked. The challenge is how to get started and get the runs on the board that build innovation momentum. Google Cloud's Nigel Watson believes machine learning and artificial intelligence (AI) offer the most straightforward way to demonstrate what digital transformation can deliver. Watson is Head of Cloud Technology Partners, Japan and Asia Pacific for Google Cloud.
This is just a small slice of how technology automation has changed over the past 20 years, and I assume we can all acknowledge that AI is gaining momentum, albeit regulatory authorities, legislators and lawyers not being fully sure how to adapt or embrace the change that's currently happening. Artificial Intelligence is here, it's the hot topic or the popular kid everyone wants to play in the park with. AI and automation are bringing us daily benefits; Internet and Big Data are becoming an essential part of both our work and private lives and we now have the capacity to collect huge sums of information too cumbersome for a person to process. But what will this future bring in terms of issues, policies and regulations? Will programmers and researchers be obliged to study ethics and morals as compulsory modules throughout their learning paths?
Simply mentioning the European Commission's General Data Protection Regulation (GDPR) is enough to send shivers down the backs of businesses which have had to make rapid changes to be ready in time for the deadline. The cutoff point for organizations to conform to the new GDPR legislation has passed, but emails are still flooding in from companies hoping that you will re-subscribe and give them consent to contact you, some online services have -- at least temporarily -- become unavailable for EU visitors, and we are likely to see disruption for some time to come as companies catch up. The new framework, which impacts all EU member states, requires businesses to be more transparent in connection to what data they collect and store from users, to report the discovery of data breaches within 72 hours, and to manage information securely. The core of the legislation was designed to bring some order to the lackluster rules surrounding data collection, the masses of information stored for no business purpose, and the constant threat of data breaches. However, many organizations have been left floundering -- unsure of where their information is, how it is recorded, what has been collected in the first place and for how long, whether or not user consent for storage has been granted, and if this data has been secured.