TECHNOLOGY

The Convergence of Semantics and Machine Learning

Published

2 years ago

December 22, 2021

The Convergence of Semantics and Machine Learning

Artificial intelligence (AI) has a long history of oscillating between two somewhat contradictory poles.

On one side, exemplified by Noam Chomsky, Marvin Minsky, Seymour Papert, and many others, is the idea that cognitive intelligence was algorithmic in nature – that there were a set of fundamental precepts that formed the foundation of language, and by extension, intelligence. On the other side were people like Donald Hebb, Frank Rosenblatt, Wesley Clarke, Henry Kelly, Arthur Bryson, Jr., and others, most not even as remotely well known, who developed over time gradient descent, genetic algorithms, back propagation and other pieces of what would become known as neural networks.

The rivalry between the two camps was fierce, and for a while, after Minsky and Papert’s fairly damning analysis of Rosenblatt’s Perceptron, one of the first neural model, it looked like the debate had been largely settled in the direction of the algorithmic approach. In hindsight, the central obstacle that both sides faced (and one that would put artificial intelligence research into a deep winter for more than a decade) was that both underestimated how much computing power would be needed for either one of the models to actually bear fruit, and it would take another fifty years (and an increase of computing factor by twenty-one orders of magnitude, around 1 quadrillion times) before computers and networks reached a point where either of these technologies was feasible.

As it turns out, both sides were actually right in some areas and wrong in others. Neural networks (and machine learning) became very effective in dealing with many problems that had been seen as central in 1964: image recognition, auto-classification, natural language processing, and systems modeling, among other areas. The ability to classify, in particular, was a critical step forward, especially given the deluge of content (from Twitter posts to movies) that benefit from this.

At the same time, however, there are echoes of Minsky and Papert’s arguments about the Perceptron in the current debate about machine learning – discoverability and verifiability are both proving to be remarkably elusive problems to solve. If it is not possible to determine why a given solution is correct, then it means that there are significant hidden variables that aren’t being properly modeled, and not knowing the limits of those variables – the places where you have discontinuities and singularities, make the model far more questionable when applied to anything but its own training data.

Additionally, you replace the problem of human intervention in developing logical (and sometimes social) structures with the often time and people-intensive operation of finding and curating large amounts of data, and it can be argued that the latter operation is in fact just a thinly disguised (and arguable less efficient) version of the former.

The algorithmic side of things, on the other hand, is not necessarily faring that much better. There are in fact two facets to the algorithmic approach – analytical and semantic. The analytical approach, which can be identified as being currently defined as Data Science, involves the use of statistical analysis (or stochastics) to determine distributions and probabilities. Stochastics’ strength arguably comes in that it can be used to determine, for a sufficiently large dataset, the likelihood of specific events occurring can be established to within a certain margin of error. However, stochastics is shifting from traditional statistical analysis to the use of Bayesian networks, in which individual variables (features) can be analyzed through graph analysis.

Semantics, on the other hand, is the utilization of network graphs connecting assertions, as well as the ability to make additional assertions (via modeling) about the assertions themselves, a process known as reification. Semantics lends itself well to more traditional modeling approaches, precisely because traditional (relational) modeling is a closed subset of the semantics model, while at the same time providing the power inherent in document-object-modeling languages (DOMs) such as exemplified by XML or JSON.

Significantly, a Bayesian network can be rendered as a semantic graph with reification, as can a decision tree. Indeed, a SPARQL query is isomorphic to a decision tree in every way that counts, as each node in a decision tree is essentially the intersection of two datasets based upon the presence of specific patterns or constraints (Hint: you want to build a compliance testing system? Use SPARQL!).

The history of software is both full of purists and less full of pragmatists. Purists put a stake in the ground regarding their own particular set of tools and languages: C++ vs. Java, Imperative vs. Declarative, SQL vs NoSQL, Perl vs. … well, just about anything, when you get right down to it. Pragmatists usually try to find a middle ground, picking and choosing the best where they can and covering their ears to all of the sturm and drang of the religious wars when they can’t. Most purists ultimately become pragmatists over the years, but because most programmers tend to become program management over the years, the actual impact of such learning is minimal.

Right now, because the incarnations of all three of these areas – neural networks, Bayesians, and semantics – are relatively new, there is a strong tendency to want to see one’s tool of choice as being the best for all potential situations. However, I’d argue that each of these are ultimately graphs or tools to work with graphs, and it is this underlying commonality that I believe will lead to a broader unification. For instance,

A machine learning pipeline is a classifier. If the labels of the classifier in the middle correspond to a given ontology, then once a given entity has been classified, a representation of that entity semantically can be assigned to the relevant patterns, shapes, classes, or rules.
A machine learning system is not an index, but as my kids would say, it is index-adjacent (what a very graph-like phrase). In essence, what you’re doing is creating a map between an instance of an unknown type and its associated class(es). The plural term is important here because a class is not a thing, it is only a labeled pattern, with inheritance, in turn, being the identification of common features between two such patterns. This map is also occasionally referred to as an inverse query, in that rather than retrieving all items that satisfy the query, you are in essence retrieving the (named) patterns that the query utilizes for one of those items.
It is possible (and relatively simple, to be honest) to create classifiers in SPARQL. This is because SPARQL essentially is looking for the existence of triple patterns, not just in terms of property existence, but in terms of often secondary and tertiary relationships. SHACL, an RDF schematic language, can be thought of as a tool for generating SPARQL based upon specific SHACL constructs (among other things) and those patterns can be very subtle.
In a similar fashion, I believe that graph analytics will end up becoming as (or even more) important compared to relational data analytics, primarily because graphs make it much easier to add multiple layers of abstraction and discoverability to any kind of stochastic process, resolving many of the same issues that machine learning tools today struggle with.
The inverse of this process is also feasible. SPARQL can be utilized with incoming streams to create a graph that serves to build training data for machine language services. Because such training data will already have been labeled and identified within the context of existing ontologies, the benefit of such a process is that the resulting classifiers already have all the pieces necessary for explainability – data provenance and annotations, established identifiers, event timestamps, and more.
One other important point – SPARQL is able to change the graphs that it works with. Inferences, in which new assertions are created based upon patterns found in existing assertions, become especially important once you incorporate service calls that allow for the processing of external content directly within the SPARQL calls themselves. One of the next major evolution points for SPARQL will be in its ability to retrieve, manipulate and produce JSON as intermediate core objects (software vendors, please take note) or as sources for RDF.
This means that a future version of SPARQL no longer has to store tabular data as RDF, but instead could store it as JSON then utilize that JSON (and associated analytics functions) to create far more sophisticated inferences with a much smaller processing footprint. For an analogous operation, take a look at the XProc XML pipeline processing languages then realize that the differences between the XSLT/XQuery pipelines and the RDF/SPARQL/SHACL pipelines are mostly skin deep.

This last point is very, very important because as the latest iterations of the Agile / DevOPS / MLOps model show, pipelines and transformations are the future. By being able to work with chained transformations (especially ones where the specific pipes within that transformation are determined based upon context rather than set a prior) such pipelines begin to look increasingly like organic cognitive processes.

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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