TECHNOLOGY

Mitigating the Impact of Data Shortage on AI Models: Strategies and Solutions

Published

11 months ago

May 18, 2023

Mitigating the Impact of Data Shortage on AI Models: Strategies and Solutions

The advent of artificial intelligence (AI) has revolutionized the way we approach complex problems in fields ranging from healthcare to finance.

One of the biggest challenges in developing AI models is the need for large amounts of data to train them. As the amount of available data increases exponentially, it is reasonable to wonder what would happen if we were to run out of data. In this article, we will explore the consequences of a data shortage for AI models, as well as possible solutions to mitigate the effects.

What Happens When AI Models Run Out of Data?

AI models are trained by feeding them large amounts of data. They learn from this data by finding patterns and relationships that allow them to make predictions or classify new data. Without enough data to train on, an AI model would not be able to learn these patterns and relationships, which would lead to a decrease in its accuracy.

Source: Research Gate

In some cases, the lack of data may also prevent an AI model from being developed in the first place. For example, in medical research, there may be limited data available on rare diseases or conditions, making it difficult to train an AI model to accurately diagnose them.

Furthermore, a lack of data could make AI models vulnerable to attacks. Adversarial attacks involve intentionally introducing subtle changes to the input data in order to fool an AI model into making incorrect predictions. If an AI model has only been trained on a limited amount of data, it may be more susceptible to these attacks.

Understanding the Consequences of Data Shortage for AI Models

The consequences of a data shortage for AI models would depend on the specific application and the amount of data that is available.

Source: Nature Magazine

Here are some possible scenarios:

Decreased Accuracy: If an AI model has not been trained on enough data, its accuracy may decrease. This could have serious consequences in fields such as healthcare or finance, where incorrect predictions could have life-changing implications.
Limited Capabilities: Without enough data, an AI model may not be able to perform certain tasks. For example, a language translation model that has not been trained on a wide range of languages may not be able to accurately translate between them.
Increased Vulnerability: As mentioned earlier, a lack of data could make an AI model more vulnerable to adversarial attacks. This could be especially concerning in applications such as autonomous vehicles or cybersecurity, where incorrect predictions could have serious consequences.

Overcoming Data Shortage: Solutions for Training Robust AI Models

While data shortage could have serious consequences for AI models, there are several solutions that could help mitigate the effects.

Source: Research Gate

Here are some possible solutions:

Data Augmentation: Data augmentation involves artificially creating new data by making subtle changes to existing data. This can help increase the amount of data available for training, and also make the AI model more robust to variations in the input data.
Transfer Learning: Transfer learning involves using a pre-trained AI model as a starting point for training a new model on a different task or dataset. This can help reduce the amount of data needed to train a new model, as the pre-trained model has already learned many of the relevant patterns and relationships.
Active Learning: Active learning involves selecting the most informative data points to label during the training process, in order to maximize the amount of information gained from each labeled example. This can help reduce the amount of labeled data needed to achieve a certain level of accuracy.
Synthetic Data: Synthetic data involves generating new data that closely mimics the characteristics of the real data. This can be useful in situations where there is a limited amount of real data available, or where the real data is difficult or expensive to collect.
Collaborative Data Sharing: Collaborative data sharing involves pooling data resources from multiple sources to create a larger, more diverse dataset. This can help increase the amount of data available for training AI models and improve their accuracy.
Human-in-the-Loop: Human-in-the-loop involves human input in the training process, such as by having human experts label or verify data. This can help ensure that the AI model is learning from high-quality data, and also improve its accuracy in certain domains where human expertise is valuable.
Active Data Collection: Active data collection involves actively collecting new data in order to expand the dataset available for training. This could involve using sensors or other devices to collect new data, or incentivizing individuals or organizations to contribute data.

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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