With the help of cutting-edge technologies, artificial intelligence is transforming today drastically. Artificial intelligence, once limited to being a distinct field of study for three decades, has expanded its reach to encompass various applications across various domains. According to Grand View Research, AI will continue transforming many industries, with a projected annual growth rate of 37.3% between 2023 and 2030. This rapid rise will highlight the future significance of AI technology.

Today, we can see quite a range of emerging AI technologies. From small businesses to enormous corporations, there is a race to adopt artificial intelligence for data mining, operational excellence, etc. Lets talk about the most recent Artificial Intelligence developments.

Machine Learning is yet another useful technology in the Artificial Intelligence domain. This technology focuses on training a machine (computer) to learn and think independently. Machine Learning typically uses many complex algorithms for training the machine.

The machine is given a set of categorized or uncategorized training data about a specific or public domain during the process. The machine then analyses the data, draws inferences, and stores them for future use. When the machine encounters any other sample data of the domain it has already learned, it uses the stored inferences to draw necessary conclusions and respond appropriately.

A flexible and robust open-source machine learning framework that offers a comprehensive ecosystem for constructing and implementing ML models, strongly emphasizing deep learning and versatile architecture.

A popular open-source machine learning framework that emphasizes dynamic computation graphs, making it suitable for research and prototyping, with strong support for neural networks and deep learning.

A high-level neural networks API that runs on top of TensorFlow, PyTorch, or other frameworks simplifies building and training deep learning models, particularly for beginners and rapid prototyping.

Communicating effectively and clearly can be challenging, but processing information for machines differs from the human brain. Natural Language Generation (NLG) is crucial in converting text into data, enabling systems to convey ideas and thoughts. NLG finds extensive applications in customer service, generating reports, and producing market summaries.

Prominent companies like Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, and Yseop offer NLG solutions. It comes as no surprise that NLG is among the top 15 cutting-edge artificial intelligence technologies.

A comprehensive library for NLP tasks, providing tools for tokenization, stemming, tagging, parsing, and more.

A popular NLP library offering efficient tokenization, part-of-speech tagging, named entity recognition, dependency parsing, and pre-trained word vectors.

A library for topic modeling, document similarity analysis, and unsupervised learning of word embeddings like Word2Vec and FastText.

A Java-based NLP library by Stanford provides many tools, including tokenization, part-of-speech tagging, parsing, and sentiment analysis.

A simple and user-friendly library built on NLTK, offering tools for tokenization, part-of-speech tagging, noun phrase extraction, and sentiment analysis.

A library for state-of-the-art transformer models like BERT, GPT, and XLNet, enabling tasks such as text classification, named entity recognition, and question answering.

A powerful library built on PyTorch, specifically designed for NLP research, providing high-level abstractions for building and evaluating deep learning models.

The corporate landscape is experiencing a remarkable upswing in the need for artificial intelligence (AI) software. However, as the significance of such software grows, the need for compatible hardware also becomes apparent. Traditional chips cannot adequately support AI models, leading to a new generation of AI chip development designed for neural networks, deep learning, and computer vision tasks.

These AI solutions encompass a range of components, including CPUs capable of handling scalable workloads, specialized silicon chips built for neural networks, and innovative neuromorphic chips. Major technology organizations like Nvidia, Qualcomm, and AMD are actively involved in creating advanced chips that can perform complex AI calculations.

A popular computer vision library that provides many tools and algorithms for image and video processing, object detection, feature extraction, and more.

An open-source machine learning framework with a powerful computer vision module, TensorFlow Object Detection API, for training and deploying object detection models.

Another popular deep learning framework that offers computer vision capabilities is through its TorchVision library, which provides tools for image classification, object detection, and semantic segmentation.

A Python library focusing on image processing tasks, offering a comprehensive collection of algorithms and functions for image enhancement, filtering, segmentation, and feature extraction.

A C++ library with Python bindings specializing in facial detection and recognition, providing pre-trained models for face detection, landmark detection, and face alignment.

A deep learning framework known for its efficiency and speed, Caffe includes a computer vision library that supports image classification, object detection, and semantic segmentation.

A user-friendly computer vision library designed for beginners, SimpleCV provides easy-to-use functions for basic image processing tasks, such as filtering, feature detection, and color tracking.

A scientific computing library for Python, SciPy includes modules for image processing that offer functions for tasks like filtering, morphological operations, image restoration, and mathematical transformations.

Many businesses need help utilizing AI, primarily due to the high costs associated with the in-house development of AI products. Consequently, there is a growing demand for outsourced AI solutions, as they offer a more cost-effective approach for small and medium-sized businesses and budget-conscious large enterprises to dip their toes into AI. By leveraging cloud-based AI services, organizations can access the benefits of artificial intelligence without the hefty investment typically required for in-house development.

Amazons AI initiatives include enhancing its consumer devices like Alexa and delivering services through AWS. Interestingly, a significant portion of AWSs business cloud services is built upon the foundation of these consumer products. As Alexa evolves and improves, its business equivalent will follow suit.

Amazon Lex offers a comprehensive solution for integrating conversational interfaces into any application. This technology is currently utilized in Alexa, empowering developers to design chatbots with advanced natural language capabilities.

With its unique hardware innovation called the Tensor Processing Unit (TPU), Google sets itself apart from other cloud providers. The TPU is a specialized chip specifically designed to enhance the performance of TensorFlow, Googles open-source machine learning platform.

While other major cloud providers offer TensorFlow, none can access TPUs, giving Google a competitive edge. TPUs boast remarkable speed improvements, with 15 to 30 times faster performance than traditional CPUs or GPUs. They deliver up to 180 teraflops of computing power, making complex machine-learning tasks significantly faster and more efficient.

In addition to its hardware advantage, Google leverages its AI capabilities from consumer-facing products to cater to business users. The powerful AI algorithms that drive Google applications like Images, Translate, Inbox (Smart Reply), and voice search in Android are accessible through Google Compute Engine, its cloud offering.

This means businesses can harness the same cutting-edge AI technology that powers Googles popular consumer applications to improve their operations and services.

Microsoft organizes its AI solutions into three categories: AI Services, AI Tools and Frameworks, and AI Infrastructure. Unlike Amazon, Microsoft also leverages some of its consumer products to build its business AI offerings.

Under the AI Services category, there are three subgroups. The first is pre-built AI capabilities, which enhance customer-facing applications like web chatbots. Cognitive Search combines Azure Search with Cognitive Services to provide advanced search capabilities. Conversational AI utilizes Azure Bot Service to enable conversational bots with enhanced features like richer dialogs, full personality customization, and voice customization.

Artificial Intelligence (AI) encompasses computational models that replicate intelligence.

The widespread adoption of AI across various sectors has already yielded many benefits. However, it is crucial for organizations implementing AI to conduct rigorous pre-release trials to identify and mitigate biases and errors. The design and models employed should be robust and capable of withstanding real-world challenges.

Organizations should establish and uphold standards while hiring experts from diverse disciplines to facilitate informed decision-making. AIs ultimate aim and future vision revolve around automating complex human activities and eradicating errors and biases.

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Unveiling the Top AI Development Technologies | by Pratik ... - DataDrivenInvestor

Tech breakthroughs have become prevalent in this digitised environment, and revolutionising the facets of our daily lives. The growth of AI has been a spectacular breakthrough, ushering in possibilities and transformations.

AI applications have found their way into industries, allowing for automation, better decision-making, and overall efficiency. However, as the world embraces digitalisation and AI, it is also confronted with the darker side of this technological revolution - the new perils of cybercrime.

Particularly in the aftermath of COVID-19, the travel industry has seen tremendous expansion and appeal. The unprecedented rebound in outbound travel has increased the exposure to cybercriminals. The travel and tourist industry has become a target of cyberattacks, suffering a variety of threats such as data breaches, ransomware attacks, and phishing efforts.

According to industry forecasts, the costs of cyberattacks would climb by a steep 15 per cent every year, reaching a staggering $10.5 trillion by 2025. This shows the intensity of the situation, emphasising the critical need for organisations to commit significant resources to strengthen their cybersecurity.

Recognising the importance, an astounding 85 per cent of SMEs have stated their intention to boost IT security investment by end-2023. That explains why investing in comprehensive cybersecurity measures is not only a must, but a strategic imperative for long-term success.

Following are a few technology advances that can help combat cybercrime:

AI and Machine Learning

AI and ML are revolutionising the field of cybersecurity by providing advanced capabilities to detect, analyze, and respond to cyber threats in real-time. These enable systems to automatically learn from vast amounts of data and identify patterns, anomalies, and potential risks that might be difficult for human operators to detect.

AI and ML algorithms can analyse network traffic, user behavior, and system logs to identify malicious activities, such as malware infections, unauthorised access attempts, or abnormal data transfers. This approach allows organisations to respond swiftly and effectively, minimising the impact of cyberattacks.

Blockchain

Blockchain technology, originally developed for cryptocurrencies like Bitcoin, offers a decentralised and tamper-resistant method of storing and verifying data. Its inherent properties, such as transparency, immutability, and consensus-based validation, make it highly suitable for enhancing cybersecurity.

In the context of cybersecurity, blockchain can be used to secure critical data, authenticate identities, and establish secure communication channels. By decentralising data storage and ensuring that information cannot be easily altered, blockchain technology adds an extra layer of protection against unauthorised access, data tampering, and insider threats.

Zero Trust architecture

Traditional network security models operate on the assumption that once a user or device gains access to the internal network, they can be trusted. However, with the increasing sophistication of cyber threats, the Zero Trust architecture has gained prominence.

Zero Trust revolves around the concept of never trust, always verify. Under this model, all users, devices, and network traffic are treated as potentially untrusted and are continuously verified and authenticated before being granted access to critical resources.

Zero Trust employs various security measures such as multi-factor authentication, strict access controls, and continuous monitoring to ensure that only authorized entities can access sensitive data and systems. By implementing Zero Trust principles, organisations can mitigate the risk of internal and external attacks, limit lateral movement, and protect their digital assets.

Staying ahead of cyber threats requires organisations to embrace evolving technologies as integral components of their cybersecurity strategies. These technologies have helped many organisations in the industry stay safe while also lowering their security management expenditures.

As businesses continue to grapple with the growing challenges of cybersecurity, those at the forefront of technological innovation will gain a competitive advantage by safeguarding their operations, reputation, and, most importantly, their invaluable data.

Suraj Tiwari

The writer is Head - Information Security, VFS Global.

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Bring on AI and Machine Learning to take on escalating cybercrime threats - Gulf News

AI has enjoyed a long hype cycle, recently reignited by the introduction and rapid adoption of OpenAIs ChatGPT. Companies are now at varying stages of AI adoption given their business goals, resources, access to expertise, and the fact that AI is being embedded in more applications and services. Irrespective of industry, AI depends on a critical mass of quality data. However, the necessary quality depends on the use case. For example, as consumers, weve all been the victim of bad data as evidenced by marketing promotions we either laugh at or delete.

In the scientific community, such as the pharmaceutical and life sciences industry, bad data can be life-threatening, so data quality must be very high.

Also, unlike many other industries, the data required to discover a novel drug molecule tends to be scarce rather than abundant.

The data scarcity prevalent in the pharmaceutical and life science industries promotes a stronger alliance between humans and machines. Over time, there has been a significant accumulation of scientific data, the understanding of which demands a high level of education. This data accrual has been quite costly, leading to a general reluctance among owners to share the information they have acquired.

The intricate nature of scientific data implies that only scientists within the same field can comprehend the deeper contexts. Therefore, the volume of data available in an appropriate context is typically limited. This scarcity makes it challenging to develop credible AI algorithms in the healthcare industry.

To counteract this data deficit, human experts play an essential role in providing context and supplementary information. This human intervention helps in the co-development of algorithms and the workflows in which these algorithms are accurately utilized.

AI hype cycles have caused fear, uncertainty, and doubt because vendors are underscoring the need for automation in white-collar settings. In the distant past, AI was firmly focused on production-line jobs impacting blue-collar workers. Back then, no one anticipated AI would impact knowledge work, especially because AI capabilities were limited to the technology of the day and in most cases, it was rule-based.

Now, we see pervasive use of AI techniques such as machine learning and deep learning that can analyze massive amounts of data at scale. Instead of following a deterministic set of rules, modern systems are probabilistic, which makes things like prediction, as opposed to just historical data analysis, possible.

In the case of pharmaceuticals and life sciences, its possible to import tags from research papers, which is helpful, but the context tends not to be stated explicitly, so scientists need to help AI understand the underlying hypothesis or scientific context. The system then learns by being rewarded for good outcomes and scientists rejecting the bad outcomes. Without a human overseeing what AI does, it can drift in a manner that makes it less accurate.

In fact, it can take several weeks or months to create molecules and test them under experimental conditions. If animals are involved, the process could take years.

Some scientists or professionals dont want to share their work with AI, particularly when they are highly educated and experienced. These people know how long research takes and how expensive it can be, and theyve grown comfortable with that over time.

Also, the pharmaceutical and life sciences industries are highly regulated, so irrespective of whether AI is used or not, there are certain processes, and certain levels of rigor required just to ensure patient safety.

Interestingly, when well-trained scientists see AI in action, it becomes abundantly clear that it can handle a million or more data points easier and faster than a human. Suddenly, it becomes clearer that AI is a valuable tool that can save time and money and enable greater precision.

However, that doesnt mean that scientists trust what they see, especially when it comes to deep learning, which includes large language models such as ChatGPT. The problem with deep learning is that it tends to be opaque, meaning that it can take an input or multiple inputs and produce a result, but the AI is unable to explain in terms understandable to humans how it arrived at that result.

Thats why theres been a loud cry for AI transparency; people want to trust the result. Scientists and auditors demand it.

One of the biggest genetic databases is 23andMe. This is the gene testing service that reveals a persons ethnicity. It has also enabled individuals to discover family members they never met, such as a set of twins, one of whom was adopted. The service offers significant entertainment value.

However, from a scientific standpoint, it doesnt offer much.

Without understanding someones medical history, understanding genetic composition can only be somewhat helpful. As an example, a brother and sister may carry the same gene that is expressed in one and dormant in the other.

The more we know about an individual, the better chance there is of choosing the compounds that will work for them and at what dosage level. Today, theres still a lot of trial and error, and doses are standardized. In short, AI will help make personalized medicine possible.

The pharmaceutical and life sciences industries are both highly competitive. About two years ago, I visited Cambridge University and noticed that big pharma companies had sent a researcher or two to learn about Cambridges experimental automation technology that utilizes AI. Big pharma companies often work with research institutions to learn about scientific discovery and to get the high-quality data they need.

Another example is Recursion Pharmaceuticals where theyre automating biology-related processes. They photograph cells after treating some molecules and then use AI algorithms to understand the images. They produce tens of terabytes of image data every day and the experimental conditions are decided by prediction models. As new data comes in, the system generates new models, and the cycle repeats automatically and continuously.

AI is transforming the ways organizations and industries operate. However, scientific disciplines require a rigorous approach that yields accurate results and provides the transparency scientists and auditors need. Since governance, privacy, and security are not inherently baked into AI, organizations with strict requirements need to be sure that the technology they utilize is both safe and accurate.

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How the Human-Machine Intelligence Partnership Is Evolving - AiThority

Image by Wolfgang Eckert from Pixabay

Human neuroplasticitythe brain's dynamic capability to rewire and adapthas been a cornerstone to what makes and keeps us human. It grants us the agility to learn new languages, empathize with others, and recover from brain injuries.

But as artificial intelligence (AI) technology advances (or evolves), are we nearing a shift in which machine intelligence mirrors human plasticity? The ground beneath us is undeniably shifting as we face a formidable contender: "techno-plasticity."

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While human neuroplasticity focuses on biological adaptations, techno-plasticity describes AI systems that can undergo real-time self-modifications. These are not rigid algorithms; they evolve, adapting to new data environments and situational variables. Let's look at liquid networks and their implications for technology and humanity.

Developed by researchers at MIT, liquid networks represent a fascinating and potentially transformative step in machine learning. Unlike traditional neural networks that are trained and deployed in a relatively static state, liquid networks are designed to continuously adapt their underlying algorithms in response to new data inputs.

This is achieved by allowing the parameters in the neural network's equations to evolve based on a nested set of differential equations.

The inspiration for this technological marvel comes from nature specifically, the microscopic nematode C. elegans, which possesses a mere 302 neurons yet exhibits incredibly complex behaviors. This biological muse has inspired the development of neural networks that adapt to changing data streams and do so in a highly resilient manner to noisy or unexpected data.

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The versatility of liquid networks is already apparent in various domains. For example, a sudden downpour that obscures camera vision is no longer an insurmountable obstacle in autonomous vehicles. These liquid networks offer a robust response to unanticipated changes.

Furthermore, they have excelled in time-series prediction tasks ranging from atmospheric chemistry to robotics, outperforming state-of-the-art algorithms and doing so with less computational overhead.

And there's another edge: The architecture of liquid networks makes them more interpretable, allowing for greater insights into their decision-making processes. This addresses a longstanding issue in AIthe black -box problemmaking these powerful networks more transparent and accountable.

While the advent of liquid networks might challenge human cognition, looking at the bigger picture is crucial: the symbiotic potential between neuroplasticity and techno-plasticity. Each brings to the table a unique set of capabilities and limitations. As AI systems like liquid networks become more plastic, humans, too, will find new avenues for cognitive expansion facilitated by AI's evolving capabilities.

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Our cognitive landscape is undergoing a seismic shift. We enter an era in which neither humans nor AI monopolize adaptability or learning. Instead, we find ourselves in a dynamic equilibrium where both forms of evolving intelligenceneuroplastic and technoplasticcontinuously evolve.

As liquid networks make their mark, raising the bar for what machine learning algorithms can achieve, we must adapt and evolve. It is not a competition but a journey of co-evolution, one in which the future of artificial and natural intelligence is continually rewritten.

The advent of techno-plasticity, particularly as manifested through liquid networks, could be a powerful catalyst for human transformation and evolution. This new frontier in AI capability may spur a symbiotic relationship in which each form of intelligencehuman and artificialcompels the other to adapt, innovate, and transcend current limitations.

Artificial Intelligence Essential Reads

As AI becomes more adept at real-time learning and adaptation, it challenges us to harness this technological prowess for societal advancement and look inward, reconsidering the scope and potential of our neuroplasticity. In essence, techno-plasticity could be the stimulus that drives us to explore uncharted territories of human cognition, creativity, and problem-solving, ultimately reshaping our understanding of what it means to be human in an age of advanced artificial intelligence.

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This new narrative calls for adaptability and a deepened understanding of the complexities. We stand on the cusp of a revolution that could usher in an era of unprecedented cognitive collaboration and exploration.

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Techno-plasticity in the Age of Artificial Intelligence - Psychology Today

They would test the algorithm further with refinements and give the dieticians 10 more patients to look at the next week. This process helped boost confidence in the algorithm to a point where it is now actually placing an order for consults in the electronic medical record (EMR).

"We're finding six to 10 patients a week who have undiagnosed malnutrition. Now, if you think about that from a family member of a child, that's a huge difference. And those things are really impactful in terms of practical AI, and that's kind of spawned other ideas, but that's been kind of one of our great use cases," Higginson explained.

Five years ago, Phoenix Children's Hospital embarked on a journey to harness the power of AI in solving clinical challenges. The traditional approach of relying on biostatisticians to develop algorithms proved to be time-consuming and often inefficient. He said the team might work on an algorithm for several months and find it does not work well in the end. So Higginson's team opted for a different path, utilizing automated machine learning. This approach involves providing a dataset to an AI system that autonomously creates the algorithm, allowing the hospital to start using it within a matter of hours, rather than weeks.

One of the key lessons learned from using AI in healthcare is that getting it right on the first attempt is a rare occurrence. Thus, an iterative approach is essential to fine-tune algorithms over time.

While there are now many vendors selling commercialized AI algorithms, Higginson said many are to generalized for the needs of his hospitals, which another reason why they have decided to develop their own, highly customized algorithms.

"One of the things I've learned with AI over the years is it doesn't translate very well. So I'm always very skeptical of vendors that tell me, 'I've got an AI model that's going to work great,' because geographic factors are a huge influence as well. There are some clinical conditions which obviously translate, but I think we've seen some recent examples where models are trained in one state, lifted somewhere else and don't work," he said.

For example, he said they created AI models on operational things like our donors and managing their employees, which require very local and customized factors that are completely unique. "Understanding how far is too far for an employee to travel into work all depends on the road density, where they are traveling from. I think the concepts and the ideas are transferable. But I would be a little skeptical of taking that black box and just lifting it somewhere else," Higginson explained.

Pediatric healthcare presents unique challenges that often require tailored solutions. At Phoenix Children's Hospital, they've developed their own patient portal, recognizing that pediatric patients and their families have distinct relationships with healthcare providers. This patient portal addresses the complex dynamics of patient relationships within families and guardianship scenarios. This includes who has access in a divorce or foster home situation, and the ages when patient information needs to be shared with the patient.

Moreover, the hospital has adapted to the post-pandemic landscape by embracing telehealth services, which have been particularly well-received by pediatric patients and their caregivers. The implementation of hybrid telehealth, where patients and their caregivers join virtual consultations, has transformed the healthcare experience for families, Higginson said.

Higginson encourages a more general application of AI in healthcare, emphasizing its adaptability to a wide range of scenarios. He used the example of AI helping determine no-show rates to better staff the emergency room. Another example is AI can be used to sift through patient emails to doctors via the patient portal to determine the most appropriate recipient within the healthcare team. This could streamline communication and enhancing efficiency so doctors can practice at the top of their license not not spend a large amount of time sorting basic email requests. Higginson said doctors tell him over and over 80% of these messages are about scheduling, medications and billing which have nothing to do with the physician.

"So how great would it be to take that message that came in and run it through a GPT prompt and ask it, which help desk should this go to?" He said.

Phoenix Children's Hospital's innovative approach to AI demonstrates the immense potential for the technology in healthcare. By adopting a strategic and iterative approach, they have successfully developed clinical algorithms that not only improve patient care, but also enhance the overall healthcare experience for pediatric patients and their families.

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Leveraging machine learning to rapidly create clinical AI algorithms - HealthExec