TECHNOLOGY

The Role of Realism in AI Development and Ethics

Published

1 year ago

April 9, 2023

The Role of Realism in AI Development and Ethics

Artificial intelligence (AI) has come a long way since the first computer was invented.

Today, AI is used in a variety of applications, from voice assistants to self-driving cars. As AI continues to advance, it raises ethical concerns about the potential impact on society. One concept that has become increasingly important in AI development and ethics is realism. Realism refers to the ability of AI to accurately represent the real world. In this article, we will explore the role of realism in AI development and ethics.

Ensuring Transparency and Accountability in AI: The Need for Realism

The ability to accurately represent the real world is one of the most important aspects of AI development. This is especially important in applications like self-driving cars, in which the AI system must recognise and respond to real-world objects and situations. If the AI system isn’t realistic, it won’t be able to accurately identify and respond to objects in its environment, which could lead to accidents.

Using large datasets to train AI models is one way AI developers can ensure realism. These datasets contain millions of images, videos, and other forms of data from which the AI system can learn about the real world. A self-driving car, for example, could be trained on a dataset of millions of images of roads, traffic lights, and other objects encountered on the road. The AI system can learn to recognise objects and situations in the real world by training on a large dataset.

Simulators are another method used by AI developers to ensure realism. Computer programmes that simulate real-world scenarios are known as simulations. A self-driving car, for example, could be tested in a simulation that mimics various weather conditions, road conditions, and traffic situations. Developers can ensure that the AI system can accurately respond to different scenarios that it may encounter in the real world by testing it in a simulation.

The Ethics of Realism in AI: Addressing Bias, Discrimination, and Fairness in AI

Source: Orient Sofware

Realism is not only important in AI development, but also in AI ethics. One of the most serious ethical concerns about AI is the possibility of AI systems making biassed or discriminatory decisions. For example, if an AI system is trained on a dataset that only contains images of white faces, it may be unable to recognise faces of other races accurately. This may result in skewed decisions, such as incorrectly labelling a person of colour as a criminal.

To address this concern, AI developers and ethicists are working to ensure that AI systems are trained on diverse datasets that accurately represent the real world. This means including images and data from a wide range of sources, including different races, genders, and ages. By ensuring that AI systems are trained on diverse datasets, developers can help to minimize the potential for bias and discrimination.

Another ethical concern about AI is the potential for AI systems to replace human workers. While AI has the potential to improve efficiency and productivity, it could also lead to job losses and economic inequality. To address this concern, ethicists and policymakers are exploring ways to ensure that AI is used in ways that benefit society as a whole, rather than just a small group of people.

Realism is also important in AI transparency and accountability. As AI systems become more complex, it becomes increasingly difficult to understand how they make decisions. This is known as the “black box” problem, where the inner workings of the AI system are opaque to human observers. To address this problem, ethicists and policymakers are exploring ways to make AI systems more transparent and accountable. One way to do this is by requiring AI developers to provide explanations for the decisions made by their AI systems. This can help to ensure that AI systems are making decisions that are fair and unbiased.

Artificial intelligence (AI) has come a long way since the first computer was invented. Today, AI is used in a variety of applications, from voice assistants to self-driving cars. As AI continues to advance, it raises ethical concerns about the potential impact on society. One concept that has become increasingly important in AI development and ethics is realism. Realism refers to the ability of AI to accurately represent the real world. In this article, we will explore the role of realism in AI development and ethics.

The Importance of Realism in AI Development

One of the most important aspects of AI development is the ability to accurately represent the real world. This is particularly important in applications such as self-driving cars, where the AI system needs to be able to recognize and respond to real-world objects and situations. If the AI system is not realistic, it may not be able to accurately identify and respond to objects in its environment, potentially leading to accidents.

One way that AI developers ensure realism is by using large datasets to train their AI models. These datasets contain millions of images, videos, and other types of data that the AI system can use to learn about the real world. For example, a self-driving car might be trained on a dataset of millions of images of roads, traffic lights, and other objects it might encounter on the road. By training on a large dataset, the AI system can learn to recognize objects and situations it might encounter in the real world.

Another way that AI developers ensure realism is by using simulations. Simulations are computer programs that simulate real-world scenarios. For example, a self-driving car might be tested in a simulation that simulates different weather conditions, road conditions, and traffic situations. By testing the AI system in a simulation, developers can ensure that it can accurately respond to different scenarios that it might encounter in the real world.

Making AI Systems Accountable to Human Observers

Realism is not just important in AI development; it is also important in AI ethics. One of the main ethical concerns about AI is the potential for AI systems to make biased or discriminatory decisions. For example, if an AI system is trained on a dataset that contains only images of white faces, it may not be able to accurately recognize faces of other races. This could lead to biased decisions, such as incorrectly identifying a person of color as a criminal.

Source: Salesforce Research

To address this concern, AI developers, policymakers and ethicists must ensure that AI systems are trained on diverse datasets that accurately represent the real world. This means including images and data from a wide range of sources, including different races, genders, and ages. By ensuring that AI systems are trained on diverse datasets, developers can help to minimize the potential for bias and discrimination.

Conclusion

Realism is a crucial concept in AI development and ethics. By ensuring that AI systems accurately represent the real world, developers can help to minimize the potential for accidents and ensure that AI systems make fair and unbiased decisions. Realism also plays a vital role in AI ethics, by minimizing the potential for biased and discriminatory decisions, ensuring that AI is used in ways that benefit society as a whole, and promoting transparency and accountability in AI systems.

As AI continues to advance, it is essential that developers and ethicists prioritize realism in their work. This means ensuring that AI systems are trained on diverse datasets that accurately represent the real world, testing them in simulations that simulate real-world scenarios, and making them transparent and accountable to human observers. By prioritizing realism, we can ensure that AI is developed and used in ways that benefit society and minimize potential harms.

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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