Technology today is evolving at such a rapid pace, enabling faster change and progress, causing an acceleration of the rate of change, until eventually it will become exponential. However, it is not only technology trends and top technologies that are evolving, a lot more has changed this year due to the outbreak of COVID-19 making IT professionals realize that their role will not stay the same in the contactless world tomorrow. And an IT professional in 2020-21 will constantly be learning, unlearning and relearning (out of necessity if not desire). What does this mean for you? It means staying current with new technology trends.
Google on Monday said that it's partnering with Siemens to advance AI deployments in industrial use cases. More specifically, Siemens is integrating Google Cloud data analytics and AI capabilities into its Digital Industries Factory Automation portfolio. The integration gives Google a major partner in the manufacturing space, one of six key verticals the cloud company is targeting. The integration, the companies said, should make it easier for manufacturers to manage factory data, run cloud-based AI and machine learning models on top of it, and deploy algorithms at the network edge. Over the next few months, the companies will have share more about the specific Google Cloud tools that will be integrated into the Siemens portfolio and offered as a joint solution, a Google spokesperson told ZDNet.
According to IDG's 2020 Cloud Computing Study, 92% of organizations have at least some sort of cloud footprint in regard to their IT environment. Therefore, traditional cloud security approaches must evolve to keep up with the dynamic infrastructure and challenges that cloud environments present – most notably, the inundation of data insights generated within the cloud. More than one-third of IT security managers and security analysts ignore threat alerts when the queue is full. This is a common issue that is driving the high demand for machine learning-based analytics, as it helps security teams sift through massive amounts of data to prioritize risks and vulnerabilities and make more informed decisions. However, a word of caution when using machine learning-based technology: the age-old garbage-in, garbage-out applies to security-focused machine learning engines.
The reason why we use the cloud so much is the bottom line: Saving money. StormForge, a start-up specializing in reducing cloud waste with machine learning (ML) and artificial intelligence (AI) has found in its recent survey that businesses waste over $17-billion a year on unused or idle cloud resources. Now, it's not that companies have an unrealistic view of what they're going to be spending. Ninety-four percent say they know, at least roughly, what their cloud spend will be each month. The bad news is they also estimate that nearly half of their cloud spend is wasted on unused or idle resources.
To say Kubernetes, everyone's top container orchestration pick, is hard to master is an understatement. Kubernetes doesn't have so much as a learning curve as it does a learning cliff. But, Canonical's MicroK8s lets you learn to climb it in your home. And, with its latest release, it's easier than ever to set up a baby Kubernetes cluster using inexpensive Raspberry Pi or NVIDIA Jetson single-board computers (SBC). MicroK8s is a tiny Kubernetes cluster platform.
The term Artificial Intelligence (AI) was used for the first time by John McCarthy during a workshop in 1956 at Dartmouth College. The first AI application programs for playing checker and chess were developed in 1951. After the '50s, AI was on the rise and fall until the 2010s. Over the years, there have been some investments in AI by vendors, universities, institutions. Sometimes, hopes were high and sometimes hopes were low.
Earlier this week, Microsoft announced its intent to acquire Nuance for $19.7 billion in its second-largest acquisition after LinkedIn. For the past 15 years, Nuance has been the largest independent speech recognition vendor servicing healthcare and enterprise customer service markets. With this acquisition, Microsoft gets serious healthcare chops, an arsenal of conversational AI assets (including voice biometrics), digital customer service technologies, and other assets like vehicle telematics and dictation. For companies using Microsoft and/or Nuance, this acquisition will provide them with more depth and breadth in the healthcare provider space, enterprise customer care, and enterprise cloud services. Microsoft is making a $19.7 billion bet on ambient digital healthcare How and why tech's big players are poised to give the industry its biggest shakeup in decades.
Here's a look at how the cloud leaders stack up, the hybrid market, and the SaaS players that run your company as well as their latest strategic moves. IBM said it will acquire process mining company myInvenio as it aims to automate more business processes. Terms of the deal weren't disclosed. IBM and myInvenio, based in Reggio Emilia, Italy, launched a partnership in November. According to IBM, myInvenio can use its platform to find the best business processes to automate with AI.
We have a vision of a Network Compute Fabric where the lines between networking and computing disappear. On the journey there, edge cloud computing provides a critical stepping-stone where computing is pushed very close to where it is needed. This distribution of computing capabilities in the network creates new challenges for its management and operation. We argue that a data-centric approach that extensively uses artificial intelligence (AI) and machine learning (ML) technologies to realize specific management functions is a good candidate to tackle these challenges. As can be seen in Figure 1, edge computing services can be provided through compute/storage resources at different locations in a network, such as on-premises at a customer/enterprise site (industrial control, for example) or at access and local/regional sites (telco operators, for example).
SK Telecom has announced it will split into two companies, in a move that it said would facilitate future growth and allow investment activities to be made more swiftly. Looking at the split, the surviving company will keep its existing telco assets while the spin-off company will take over operations of SK Telecom's various tech subsidiaries, such as the chip giant SK Hynix, e-commerce company 11StreetCo, and ride-sharing app T Map Mobility, among others. This means the surviving company will continue to run SK Telecom, the country's largest telco, which currently has a subscriber base of 6.35 million 5G customers, amounting to 46.5% of the market. It will also own subsidiaries, such as SK Broadband, which is the second largest broadband service provider in Korea, and expand investment in areas such as cloud, data centre, and subscription-based services. "The surviving company will strengthen its position as the leading telecommunications company with AI technology at its core," SK Telecom said in a statement.