Having claimed North America and Europe, the cloud giants hope to add Latin America and Africa to their empires
OPINION When the major cloud providers warned of slowing customer demand earlier this quarter, many expected them to pull back on their capex expenditures until the latest macroeconomic headwinds had blown over. Only, they didn’t.
Week after week, the major cloud providers have pushed ahead. They’ve announced new capacity, availability zones, and regions across Central and South America and sub-Saharan Africa – all markets that have undergone an explosion of demand for cloud services over the past two years.
Amazon Web Services (AWS), Microsoft Azure, and Google Cloud overwhelmingly dominate the US and European markets – and if they have their way, they’ll control an even larger stake in these emerging markets too. Read More
Tag Archives: Cloud
Leveraging Multi-Cloud Clusters in Real-World Scenarios
Finding the balance between edge AI vs. cloud AI
AI at the edge allows real-time machine learning through localized processing, allowing for immediate data processing, detailed security and heightened customer experience. At the same time, many enterprises are looking to push AI into the cloud, which can reduce barriers to implementation, improve knowledge sharing and support larger models. The path forward lies in finding a balance that takes advantage of cloud and edge strengths.
…In a perfect world, we’d centralize all workloads in the cloud for simplicity and scale, however, factors such as latency, bandwidth, autonomy, security and privacy are necessitating more AI models to be deployed at the edge, proximal to the data source. Read More
The Edge is the new center: Edge computing enables emerging technologies (IoT, 5G and AI) for the new data decade
For decades now, most data-driven innovation has taken place in centralized glass-walled rooms, data centers and mega clouds. The gravity these facilities create pulls data inward for processing, and then the resulting value is pushed back out.
Today, the world is changing, as a new digital future takes shape. We are entering an era in which the bulk of new data will be processed at the Edge, outside of corporate and cloud data centers. Read More
The Birth Of The Cloud-Enabled Hybrid-AI Robotics Platform
I continue to think that Robots are the next big technology trend. The COVID-19 event seems to make this even more likely because of the need to keep people apart from each other to prevent the virus’ spread.
This week Qualcomm put on a presentation on this topic with their focus being on 5G connecting these robots to centralized AI resources. This presentation was designed to showcase their Robotics RB5 platform but is also showcased a different way to create robots so that they are more reliable, smarter, and less expensive to build. Read More
Serverless Cloud Computing Will Drive Explosive Growth In AI-Based Innovation
As we look back at the past decade of innovation in the private and the public cloud space, led by Amazon, Microsoft, Google and IBM, the most significant emerging trend we see is the drive toward serverless computing and the appliance model.
Serverless cloud computing makes innovation more affordable and accessible to all companies and teams – regardless of size and resources. Read More
How Fog Computing is changing the BigData paradigm for IoT device?
The new era of BigData and advances in technology have made significant transitions towards the high functionality of IoT devices. The popularity of IoT devices has led to more easier methods for BigData collection, analysis, and distribution at a rapid rate. According to a report by Statista, by 2020, there will be 30 billion IoT devices worldwide, with this number set to exceed over 75 billion by 2025, Statistically, also, BigData accumulation over IoT devices and networks is clearly visible and to solve this problem, various computing methods are already popular. There are methods like quantum computing, cloud computing, edge/fog computing.
Though Quantum computing has a bright prospect, it has a long way to go, meanwhile, cloud computing is already a popular analytic method among developers and data scientists. In 2014, a new method, ‘fogging’ was first termed at Cisco. Fogging is better known as edge computing/fog computing. Big data analytics tools like Hadoop helps in reducing the cost of storage. This further increases the efficiency of the business. Read More
More efficient security for cloud-based machine learning
A novel encryption method devised by MIT researchers secures data used in online neural networks, without dramatically slowing their runtimes. This approach holds promise for using cloud-based neural networks for medical-image analysis and other applications that use sensitive data.
Outsourcing machine learning is a rising trend in industry. Major tech firms have launched cloud platforms that conduct computation-heavy tasks, such as, say, running data through a convolutional neural network (CNN) for image classification. Resource-strapped small businesses and other users can upload data to those services for a fee and get back results in several hours.
But what if there are leaks of private data? In recent years, researchers have explored various secure-computation techniques to protect such sensitive data. But those methods have performance drawbacks that make neural network evaluation (testing and validating) sluggish — sometimes as much as million times slower — limiting their wider adoption. Read More
MIT can secure cloud-based AI without slowing it down
It’s rather important to secure cloud-based AI systems, especially when they they use sensitive data like photos or medical records. To date, though, that hasn’t been very practical — encrypting the data can render machine learning systems so slow as to be virtually unusable. MIT thankfully has a solution in the form of GAZELLE, a technology that promises to encrypt convolutional neural networks without a dramatic slowdown. The key was to meld two existing techniques in a way that avoids the usual bottlenecks those methods create. Read More
No cloud required: Why AI’s future is at the edge
For all the promise and peril of artificial intelligence, there’s one big obstacle to its seemingly relentless march: The algorithms for running AI applications have been so big and complex that they’ve required processing on powerful machines in the cloud and data centers, making a wide swath of applications less useful on smartphones and other “edge” devices.
Now, that concern is quickly melting away, thanks to a series of breakthroughs in recent months in software, hardware and energy technologies that are rapidly coming to market.
That’s likely to drive AI-driven products and services even further away from a dependence on powerful cloud-computing services and enable them to move into every part of our lives — even inside our bodies. In turn, that could finally usher in what the consulting firm Deloitte late last year called “pervasive intelligence,” shaking up industries in coming years as AI services become ubiquitous. Read More
Rick's Cafe AI 