Ta kontakt med oss


The Convergence of Semantics and Machine Learning


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.

Read More

Klicka för att kommentera

Lämna ett svar

Din e-postadress kommer inte publiceras. Obligatoriska fält är märkta *


Radware launches a spinoff of its cloud security business


Cloud Computing News

Duncan is an award-winning editor with more than 20 years experience in journalism. Having launched his tech journalism career as editor of Arabian Computer News in Dubai, he has since edited an array of tech and digital marketing publications, including Computer Business Review, TechWeekEurope, Figaro Digital, Digit and Marketing Gazette.

Radware, a provider of cyber security and application delivery solutions, has revealed the spinoff of its Cloud Native Protector (CNP) business to form a new company called SkyHawk Security.

To accelerate Skyhawk Security’s development and growth opportunities, an affiliate of Tiger Global Management will make a $35 million strategic external investment, resulting in a valuation of $180 million. Tiger Global Management is a leading global technology investment firm focused on private and public companies in the internet, software, and financial technology sectors.

Skyhawk Security is a leader in cloud threat detection and protects dozens of the world’s leading organizations using its artificial intelligence and machine learning technologies. Its Cloud Native Protector provides comprehensive protection for workloads and applications hosted in public cloud environments. It uses a multi-layered approach that covers the overall security posture of the cloud and threats to individual workloads. Easy-to-deploy, the agentless solution identifies and prevents compliance violations, cloud security misconfigurations, excessive permissions, and malicious activity in the cloud.

“We recognize the growing opportunities in the public cloud security market and are planning to capitalize on them,” said Roy Zisapel, Radware’s president and CEO. “We look forward to partnering with Tiger Global Management to scale the business, unlock even more security value for customers, and position Skyhawk Security for long-term success.”

The spinoff, which adds to Radware’s recently announced strategic cloud services initiative, further demonstrates the company’s ongoing commitment to innovation. Skyhawk Security will have the ability to operate with even greater sales, marketing, and product focus as well as speed and flexibility. Current and new CNP customers will benefit from future product development efforts, while CNP services for existing customers will continue without interruption.

Radware does not expect the deal to materially affect operating results for the second quarter or full year of 2022.


Source link

Fortsätt läsa


How Sports Organizations Are Using AR, VR and AI to Bring Fans to The Game


How Sports Organizations Are Using AR, VR and AI to Bring Fans to The Game

AR, VR, and AI in sports are changing how fans experience and engage with their favorite games.

That’s why various organizations in the sports industry are leveraging these technologies to provide more personalized and immersive digital experiences.

How do you get a sports fan’s attention when there are so many other entertainment options? By using emerging technologies to create unforgettable experiences for them! Innovative organizations in the sports industry are integrating AR, VR and AI in sports marketing and fan engagement strategies. Read on to discover how these innovative technologies are being leveraged to enhance the game-day experience for sports fans.  



AR is computer-generated imagery (CGI) that superimposes digitally created visuals onto real-world environments. Common examples of AR include heads-up displays in cars, navigation apps and weather forecasts. AR has been around for decades, but only recently has it become widely available to consumers through mobile devices. One of the best ways sports organizations can use AR is to bring historical moments to life. This can help fans connect to the past in new ways, increase brand affinity and encourage them to visit stadiums to see these experiences in person. INDE has done just that, creating an augmented reality experience that lets fans meet their favorite players at the NFL Draft.


VR is a computer-generated simulation of an artificial environment that lets you interact with that environment. You experience VR by wearing a headset that transports you to a computer-generated environment and lets you see, hear, smell, taste, and touch it as if you were actually there. VR can be especially impactful for sports because it lets fans experience something they would normally not be able to do. Fans can feel what it’s like to be a quarterback on the field, a skier in a race, a trapeze artist, or any other scenario they’d like. The VR experience is fully immersive, and the user is able to interact with the content using hand-held controllers. This enables users to move around and explore their virtual environment as if they were actually present in it.


Artificial intelligence is machine intelligence implemented in software or hardware and designed to complete tasks that humans usually do. AI tools can manage large amounts of data, identify patterns and make predictions based on that data. AI is already influencing all aspects of sports, from fan experience to talent management. Organizations are using AI to power better digital experiences for fans. They’re also using it to collect and analyze data about fan behavior and preferences, which helps organizers better understand what their customers want. AI is also changing the game on the field, with organizations using it to make better decisions in real time, improve training and manage player health. Much of this AI is powered by machine learning, which is a type of AI that uses data to train computer systems to learn without being programmed. Machine learning is the reason why AI is able to evolve and get better over time — it allows AI systems to adjust and improve based on new data.


VR and AR are both incredible technologies that offer unique benefits. VR, for example, is an immersive experience that allows you to fully imagine and explore another virtual space. AR, on the other hand, is a technology that allows you to see and interact with the real world while also being able to see digital content superimposed on top of it. VR and AR are both rapidly evolving and can have a significant impact on sports marketing. By using both technologies, brands and sporting organizations can create experiences that bridge the real and virtual. This can help sports marketers create more engaging experiences that truly immerse their customers in the game.

Technologies like AR, VR and AI in sports are making it possible for fans to enjoy their favorite games in entirely new ways. AR, for example, can help sports lovers experience historical moments, VR lets them immerse themselves in the game, and AI brings them more personalized and immersive digital experiences. The best part is that sports fans can also use these technologies to interact with one another and feel even more connected. 

Source link

Fortsätt läsa


The Dark Side of Wearable Technology


The Dark Side of Wearable Technology

Wearable technology, such as smartwatches, fitness trackers, and other devices, has become increasingly popular in recent years.

These devices can provide a wealth of information about our health and activity levels, and can even help us stay connected with our loved ones. However, there is also a dark side to wearable technology, including issues related to privacy, security, and addiction. In this article, we will explore some of the darker aspects of wearable technology and the potential risks associated with these devices.

1. Privacy Concerns



Source: Deloitte

Wearable technology can collect and transmit a significant amount of personal data, including location, health information, and more. This data is often shared with third parties, such as app developers and advertisers, and can be used to track and target users with personalized advertising. Additionally, many wearable devices lack robust security measures, making them vulnerable to hacking and data breaches. This can put users’ personal information at risk and expose them to identity theft and other cybercrimes.

2. Security Risks


Source: MDPI

Wearable technology can also pose security risks, both to the individual user and to organizations. For example, hackers can use wearable devices to gain access to sensitive information, such as financial data or personal contacts, and use this information for malicious purposes. Additionally, wearable technology can be used to gain unauthorized access to secure areas, such as buildings or computer systems, which can be a major concern for organizations and governments.

3. Addiction Issues


Source: Very Well Mind

The constant connectivity and access to information provided by wearable technology can also lead to addiction. The constant notifications and the ability to check social media, emails and other apps can create a constant need to check the device, leading to addiction-like symptoms such as anxiety, insomnia and depression.

4. Health Risks


Source: RSSB 

Wearable technology can also pose health risks, such as skin irritation and allergic reactions caused by the materials used in the device. Additionally, the constant use of wearable technology can lead to poor posture and repetitive stress injuries, such as carpal tunnel syndrome. It is important for users to be aware of these risks and to take steps to protect their health, such as taking regular breaks from using the device and practicing good ergonomics.


Wearable technology has the potential to be a powerful tool for improving our health, fitness, and overall well-being. However, it is important to be aware of the darker aspects of wearable technology and the potential risks associated with these devices. By understanding the privacy, security, addiction, and health risks associated with wearable technology, users can take steps to protect themselves and their personal information. Additionally, by being aware of these risks, organizations can take steps to protect their employees and customers from the potential negative effects of wearable technology.

Source link

Fortsätt läsa