TECHNOLOGY

The State of Computer Vision in 2023: Opportunities and Challenges Ahead

Published

1 year ago

April 4, 2023

The State of Computer Vision in 2023: Opportunities and Challenges Ahead

The field of computer vision has come a long way since the first experiments with image recognition in the 1960s.

Computer vision technology is being used in a wide range of applications, from self-driving cars to healthcare to security systems. In 2023, the state of computer vision is strong, with recent advances in deep learning, neural networks, and image processing pushing the boundaries of what’s possible. But there are also significant challenges facing the field, including ethical considerations and the need for more diverse and representative data sets. In this article, we’ll explore the state of computer vision in 2023, the opportunities that lie ahead, and the challenges that must be overcome to unlock its full potential.

Recent Advances in Computer Vision

Source: ML2Grow

In recent years, deep learning has emerged as a powerful tool for computer vision. Deep learning algorithms, which use artificial neural networks to mimic the way the human brain processes information, have been used to achieve breakthroughs in image recognition and classification. For example, in 2012, a deep learning algorithm called AlexNet achieved a record-breaking 15.3% error rate in the ImageNet Large Scale Visual Recognition Challenge, beating the previous best result by a wide margin.

Since then, deep learning has continued to improve, with new algorithms and architectures pushing the limits of what’s possible. For example, in 2020, researchers at Google introduced a new deep learning architecture called EfficientNet, which achieved state-of-the-art results on a range of image classification tasks while using fewer parameters than previous models. EfficientNet has since been adopted by a wide range of companies and researchers, highlighting the power of deep learning in computer vision.

Another area of recent progress in computer vision is in image processing. Advances in image processing algorithms have made it possible to extract more information from images, such as detecting and tracking objects in real-time video streams. For example, in 2018, researchers at Stanford University developed a real-time object detection algorithm called YOLO (You Only Look Once), which achieved state-of-the-art performance on a range of benchmarks. YOLO has since been used in a wide range of applications, including self-driving cars and security systems.

Opportunities for Computer Vision

The recent advances in computer vision have opened up a range of new opportunities in a variety of industries. Here are a few examples:

Healthcare: Computer vision can be used in a wide range of healthcare applications, such as diagnosing diseases from medical images, monitoring patients remotely, and improving surgical outcomes. For example, in 2018, researchers at Stanford University developed a deep learning algorithm that can diagnose skin cancer with the same accuracy as human dermatologists.

Retail: Computer vision can be used in retail to improve the shopping experience, such as by automatically detecting and recognizing products, or by tracking customer behavior to make personalized recommendations. For example, Amazon Go stores use computer vision to track customers as they move around the store and automatically charge them for the products they take.

Security: Computer vision can be used in security systems to detect and track intruders, or to recognize individuals based on their facial features. For example, the Chinese government has developed a nationwide surveillance system called Skynet, which uses facial recognition technology to track individuals and monitor their behavior.

Challenges Facing Computer Vision

While the opportunities for computer vision are significant, there are also significant challenges facing the field. Here are a few examples:

Ethics: Computer vision can be used for both good and bad purposes, such as in surveillance systems that infringe on privacy or in facial recognition systems that perpetuate biases. It’s important for researchers and developers to consider the ethical implications of their work and to ensure that their systems are designed to respect individual rights and promote social justice.

Data Bias: Computer vision algorithms are only as good as the data they are trained on. If the data is biased or unrepresentative, the algorithm will learn those biases and perpetuate them in its predictions. This can lead to unfair and discriminatory outcomes, particularly in applications like facial recognition, where biases can disproportionately impact marginalized communities. To overcome this challenge, researchers and developers must ensure that their data sets are diverse, representative, and free from bias.

Adversarial Attacks: Computer vision algorithms can also be vulnerable to adversarial attacks, where an attacker deliberately manipulates an image or video to deceive the algorithm. Adversarial attacks can be used to trick security systems, misclassify objects, or even cause self-driving cars to crash. To address this challenge, researchers are developing new algorithms and techniques that can detect and defend against adversarial attacks.

Hardware Limitations: Computer vision algorithms can be computationally expensive, requiring large amounts of processing power and memory. This can limit their scalability and practicality in real-world applications. To overcome this challenge, researchers are developing more efficient algorithms and hardware architectures, such as specialized chips designed specifically for deep learning.

What is the Future of Computer Vision?

According to Allied Market Research, the computer vision market has been expanding across multiple industries in the past years, leading to an expected growth of $17.4 billion in revenue by 2023 and $41.11 billion by 2030.

Source: HQ Software

The state of computer vision in the future is strong, with recent advances in deep learning, neural networks, and image processing pushing the boundaries of what’s possible. Computer vision is being used in a wide range of applications, from healthcare to retail to security systems, and holds tremendous promise for the future. Nonetheless, there are also significant challenges facing the field, including ethical considerations, data bias, adversarial attacks, and hardware limitations. To unlock the full potential of computer vision, researchers and developers must continue to address these challenges and ensure that their systems are designed to promote fairness, transparency, and social justice.

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TECHNOLOGY

Next-gen chips, Amazon Q, and speedy S3

Published

5 months ago

December 1, 2023

Max

AWS re:Invent, which has been taking place from November 27 and runs to December 1, has had its usual plethora of announcements: a total of 21 at time of print.

Perhaps not surprisingly, given the huge potential impact of generative AI – ChatGPT officially turns one year old today – a lot of focus has been on the AI side for AWS’ announcements, including a major partnership inked with NVIDIA across infrastructure, software, and services.

Yet there has been plenty more announced at the Las Vegas jamboree besides. Here, CloudTech rounds up the best of the rest:

Next-generation chips

This was the other major AI-focused announcement at re:Invent: the launch of two new chips, AWS Graviton4 and AWS Trainium2, for training and running AI and machine learning (ML) models, among other customer workloads. Graviton4 shapes up against its predecessor with 30% better compute performance, 50% more cores and 75% more memory bandwidth, while Trainium2 delivers up to four times faster training than before and will be able to be deployed in EC2 UltraClusters of up to 100,000 chips.

The EC2 UltraClusters are designed to ‘deliver the highest performance, most energy efficient AI model training infrastructure in the cloud’, as AWS puts it. With it, customers will be able to train large language models in ‘a fraction of the time’, as well as double energy efficiency.

As ever, AWS offers customers who are already utilising these tools. Databricks, Epic and SAP are among the companies cited as using the new AWS-designed chips.

Zero-ETL integrations

AWS announced new Amazon Aurora PostgreSQL, Amazon DynamoDB, and Amazon Relational Database Services (Amazon RDS) for MySQL integrations with Amazon Redshift, AWS’ cloud data warehouse. The zero-ETL integrations – eliminating the need to build ETL (extract, transform, load) data pipelines – make it easier to connect and analyse transactional data across various relational and non-relational databases in Amazon Redshift.

A simple example of how zero-ETL functions can be seen is in a hypothetical company which stores transactional data – time of transaction, items bought, where the transaction occurred – in a relational database, but use another analytics tool to analyse data in a non-relational database. To connect it all up, companies would previously have to construct ETL data pipelines which are a time and money sink.

The latest integrations “build on AWS’s zero-ETL foundation… so customers can quickly and easily connect all of their data, no matter where it lives,” the company said.

Amazon S3 Express One Zone

AWS announced the general availability of Amazon S3 Express One Zone, a new storage class purpose-built for customers’ most frequently-accessed data. Data access speed is up to 10 times faster and request costs up to 50% lower than standard S3. Companies can also opt to collocate their Amazon S3 Express One Zone data in the same availability zone as their compute resources.

Companies and partners who are using Amazon S3 Express One Zone include ChaosSearch, Cloudera, and Pinterest.

Amazon Q

A new product, and an interesting pivot, again with generative AI at its core. Amazon Q was announced as a ‘new type of generative AI-powered assistant’ which can be tailored to a customer’s business. “Customers can get fast, relevant answers to pressing questions, generate content, and take actions – all informed by a customer’s information repositories, code, and enterprise systems,” AWS added. The service also can assist companies building on AWS, as well as companies using AWS applications for business intelligence, contact centres, and supply chain management.

Customers cited as early adopters include Accenture, BMW and Wunderkind.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

TECHNOLOGY

HCLTech and Cisco create collaborative hybrid workplaces

Published

5 months ago

November 29, 2023

Max

Digital comms specialist Cisco and global tech firm HCLTech have teamed up to launch Meeting-Rooms-as-a-Service (MRaaS).

Available on a subscription model, this solution modernises legacy meeting rooms and enables users to join meetings from any meeting solution provider using Webex devices.

The MRaaS solution helps enterprises simplify the design, implementation and maintenance of integrated meeting rooms, enabling seamless collaboration for their globally distributed hybrid workforces.

Rakshit Ghura, senior VP and Global head of digital workplace services, HCLTech, said: “MRaaS combines our consulting and managed services expertise with Cisco’s proficiency in Webex devices to change the way employees conceptualise, organise and interact in a collaborative environment for a modern hybrid work model.

“The common vision of our partnership is to elevate the collaboration experience at work and drive productivity through modern meeting rooms.”

Alexandra Zagury, VP of partner managed and as-a-Service Sales at Cisco, said: “Our partnership with HCLTech helps our clients transform their offices through cost-effective managed services that support the ongoing evolution of workspaces.

“As we reimagine the modern office, we are making it easier to support collaboration and productivity among workers, whether they are in the office or elsewhere.”

Cisco’s Webex collaboration devices harness the power of artificial intelligence to offer intuitive, seamless collaboration experiences, enabling meeting rooms with smart features such as meeting zones, intelligent people framing, optimised attendee audio and background noise removal, among others.

Tags: Cisco, collaboration, HCLTech, Hybrid, meetings

TECHNOLOGY

Canonical releases low-touch private cloud MicroCloud

Published

5 months ago

November 28, 2023

Max

Canonical has announced the general availability of MicroCloud, a low-touch, open source cloud solution. MicroCloud is part of Canonical’s growing cloud infrastructure portfolio.

It is purpose-built for scalable clusters and edge deployments for all types of enterprises. It is designed with simplicity, security and automation in mind, minimising the time and effort to both deploy and maintain it. Conveniently, enterprise support for MicroCloud is offered as part of Canonical’s Ubuntu Pro subscription, with several support tiers available, and priced per node.

MicroClouds are optimised for repeatable and reliable remote deployments. A single command initiates the orchestration and clustering of various components with minimal involvement by the user, resulting in a fully functional cloud within minutes. This simplified deployment process significantly reduces the barrier to entry, putting a production-grade cloud at everyone’s fingertips.

Juan Manuel Ventura, head of architectures & technologies at Spindox, said: “Cloud computing is not only about technology, it’s the beating heart of any modern industrial transformation, driving agility and innovation. Our mission is to provide our customers with the most effective ways to innovate and bring value; having a complexity-free cloud infrastructure is one important piece of that puzzle. With MicroCloud, the focus shifts away from struggling with cloud operations to solving real business challenges” says

In addition to seamless deployment, MicroCloud prioritises security and ease of maintenance. All MicroCloud components are built with strict confinement for increased security, with over-the-air transactional updates that preserve data and roll back on errors automatically. Upgrades to newer versions are handled automatically and without downtime, with the mechanisms to hold or schedule them as needed.

With this approach, MicroCloud caters to both on-premise clouds but also edge deployments at remote locations, allowing organisations to use the same infrastructure primitives and services wherever they are needed. It is suitable for business-in-branch office locations or industrial use inside a factory, as well as distributed locations where the focus is on replicability and unattended operations.

Cedric Gegout, VP of product at Canonical, said: “As data becomes more distributed, the infrastructure has to follow. Cloud computing is now distributed, spanning across data centres, far and near edge computing appliances. MicroCloud is our answer to that.

“By packaging known infrastructure primitives in a portable and unattended way, we are delivering a simpler, more prescriptive cloud experience that makes zero-ops a reality for many Industries.“

MicroCloud’s lightweight architecture makes it usable on both commodity and high-end hardware, with several ways to further reduce its footprint depending on your workload needs. In addition to the standard Ubuntu Server or Desktop, MicroClouds can be run on Ubuntu Core – a lightweight OS optimised for the edge. With Ubuntu Core, MicroClouds are a perfect solution for far-edge locations with limited computing capabilities. Users can choose to run their workloads using Kubernetes or via system containers. System containers based on LXD behave similarly to traditional VMs but consume fewer resources while providing bare-metal performance.

Coupled with Canonical’s Ubuntu Pro + Support subscription, MicroCloud users can benefit from an enterprise-grade open source cloud solution that is fully supported and with better economics. An Ubuntu Pro subscription offers security maintenance for the broadest collection of open-source software available from a single vendor today. It covers over 30k packages with a consistent security maintenance commitment, and additional features such as kernel livepatch, systems management at scale, certified compliance and hardening profiles enabling easy adoption for enterprises. With per-node pricing and no hidden fees, customers can rest assured that their environment is secure and supported without the expensive price tag typically associated with cloud solutions.

Tags: automation, Canonical, MicroCloud, private cloud