• Pain Distraction and Management

Virtual reality has demonstrated enormous promise in diverting patients from severe discomfort during medical operations. Healthcare professionals can minimize the need for traditional pain management procedures by effectively reducing patients’ perception of pain through the use of virtual environments.

• Surgical Training and Simulation

Surgeons can receive training on a safe and affordable platform with VR-based surgical simulators. Surgeons can perform difficult procedures in an immersive environment, hone their abilities, and become acquainted with novel surgical techniques owing to these simulators.

• Phobia Treatment

Virtual reality has shown promise as a treatment for anxiety disorders and phobias. Therapists can help patients overcome their concerns by gradually acclimating them to virtual circumstances that trigger their phobias in a safe atmosphere.

• Physical Rehabilitation

VR technology is being applied to improve programs for physical rehabilitation. Patients can participate in engaging and stimulating activities that facilitate their recuperation from surgeries or accidents by use of interactive virtual reality workouts.

• Medical Education

VR is transforming medical education by offering immersive learning experiences for students and professionals. From exploring 3D anatomical models to interactive medical case simulations, VR is revolutionizing the way medical knowledge is imparted and retained.

• Stress Reduction

Healthcare settings can be stressful for both patients and medical staff. VR technology provides a tool for relaxation and stress reduction through immersive relaxation experiences and mindfulness exercises.

• Telemedicine and Remote Consultations

By building realistic virtual worlds that allow patients and healthcare providers to interact and communicate in real-time, virtual reality (VR) is enabling telehealth and remote consultations by removing the boundaries of physical distance.

Conclusion

The mentioned uses of VR in healthcare only scratch the surface of its enormous promise. VR technology has the potential to revolutionize therapy, medical education, and patient care as it develops. Healthcare practitioners may transform the way healthcare is provided, improve patient outcomes, and raise the standard of care by adopting these immersive innovations.

We must keep up with these advancements as thought leaders in the B2B market and look for ways to incorporate VR technologies into healthcare goods and services. Through the utilization of immersive innovations, business-to-business (B2B) organizations can significantly influence the direction of healthcare delivery and promote patient-centred care.

The post The VR Healthcare Revolution: Exploring the Next Wave of Immersive Innovations first appeared on AITechTrend.

More Businesses Are Incorporating AI Into Online Shopping

Currently, Amazon struggles with a tremendous number of returns. Its customers often order one item in multiple sizes and return the rest because they know online sizing is inconsistent. The e-commerce giant developed a new AI model in late 2023 to reduce this practice.

Amazon is one of the latest businesses to find a novel use for AI in online shopping. It recently unveiled Fit Insights — its new large language model — to reduce returns. This algorithm sifts through reviews to match customer feedback to product page sizing charts. It aims to make the listed measurements more accurate.

The Fit Insights model is one of numerous developments in the e-commerce sector — many other ground-breaking algorithms exist. Since AI is becoming less of a trend and more of an expectation, similar innovations are becoming more common.

How Does AI Fit Into the Online Shopping Experience?

Small and medium-sized businesses (SMBs) are using AI to improve every aspect of the online shopping experience.

Product Pages

Many businesses use generative AI — an algorithm that can create images, audio or text — for their product pages. It can write descriptions and create pictures in any style imaginable. This tool benefits SMBs who want to make their listings more uniform.

Order Processing

Most SMBs waste time organizing, sorting and verifying purchases. For instance, grocery retailers spend as much as 16% of their fulfillment time manually filling online orders. Using algorithms, they can automate the administrative work. They can even let AI-powered robots do the tasks for them.

Since algorithms can constantly monitor orders, they can update inventory counts in real time. Additionally, they can provide customers with accurate shipping estimations using location, weather and transportation data. 

Reviews

One of AI’s best features is its ability to process massive amounts of information in seconds. SMBs can use it to extract the most common words or phrases in reviews, showcasing the highlights for potential customers.

AI can also eliminate fraud. It can track an account’s age, order numbers, average rating and review frequency to determine how likely it is to be fake. Small businesses benefit from this use case because a few bad reviews can easily dominate their product pages.

Customer Service

E-commerce retailers use AI primarily for customer service purposes. Chatbots are common because they work 24/7 and can hold multiple conversations simultaneously, meaning businesses no longer need a queue.

A frequently-asked-questions page is another common use for AI in e-commerce customer service. Machine learning models can constantly update, so the information on these web pages always reflects what people genuinely want to know.

Hyper-Personalization

Hyper-personalized recommendations are the future of online shopping since they benefit businesses as much as consumers. In fact, e-commerce retailers are 35% more likely to make a sale because of them. Since some AI constantly learn, they always offer relevant suggestions.

Virtual try-on is another type of personalization. Instead of looking at sizing charts and pictures of mannequins, customers can use AI to see exactly how an item would look on them. The same technology can work for pets or rooms.

Most people automatically go to the search bar when shopping online. AI can improve the typical auto-fill process by using consumers’ browsing history. Also, it can improve the filtering process by using their behavioral data to make the “most relevant” tag more accurate.

How Is AI Revolutionizing the Online Shopping Experience?

Even though online shopping has been possible for ages, customers report many of the same pain points. It lacks standardization and personalization — people’s most common complaints revolve around listing inconsistencies, impersonal interactions and irrelevant recommendations.

Finally, you can innovate with AI. For instance, algorithms can now show people exactly what they would look like in clothing without needing a fitting room or measuring tape. Many e-commerce retailers have developed similar novel solutions to common pain points.

Take Amazon’s solution to inconsistent sizing, for example. Its new Fit Insights algorithm is revolutionary because it ensures all clothing — no matter the brand or country of origin — has accurate measurements. This creation is a game-changer for people who shop online.

Another fascinating innovation revolves around automation. Traditionally, most SMBs have fulfilled orders, updated inventory and handled customer complaints manually. Now, AI can handle those processes for them, letting them prioritize more essential tasks.

When shopping online, people cannot walk through aisles. Since they often search intentionally instead of browsing casually, they do not make as many impulse or unplanned purchases. However, AI can now hyper-personalize their experience — data-driven recommendations make cross-selling and upselling more profitable than ever.

How SMBs Benefit from Using AI for Online Shopping

You can benefit in many ways from leveraging the latest AI-driven e-commerce innovations. They can boost sales, improve their reputations, increase customer satisfaction and save money just by incorporating an algorithm into their operations.

While many SMBs accept products back, they lose money as a result. In fact, returns accounted for $817 billion in losses in 2022, a $56 billion increase from 2021. Even if they do not issue refunds, they still have to pay for shipping and restocking — and customers are not willing to foot the bill.

Fortunately, AI can minimize return-related costs in numerous ways. For starters, it can detect and prevent fraudulent or abusive return attempts. Also, it can improve the ordering process to increase the chances customers will be pleased with the original product.

AI can even lower overhead costs. Although an algorithm costs money to train and run, it can do the job of multiple people during holidays and off-hours — and it does not need daily breaks or an annual salary. You can use it to achieve higher productivity while shrinking their labor costs.

Most importantly, AI dramatically improves the online shopping experience, improving customer satisfaction and reducing churn rates. It makes the process more convenient, personalized and faster — things customers have wanted for years. 

Tips to Consider for SMBs Using AI for Online Shopping

Although using AI for online shopping can be beneficial, you must understand how to implement it. Properly training and deploying an algorithm is complex. Besides, a carefully thought-out plan has a much greater chance of long-term success.

Pain point and goal identification are the first things you should do before incorporating an algorithm into your e-commerce processes. Where do customers think you need to improve? What do you want to get out of AI? Carefully consider these questions before moving forward.

You should also strongly consider what kind of AI you need. Every type has unique strengths and weaknesses. For example, generative models are great for quickly producing content but get expensive if they are customer-facing because they are resource-intensive.

AI Is the Future of Online Shopping

As it stands, AI represents the future. It has revolutionized what customers feel online shopping should be — and it will never be the same. Algorithms will shape people’s expectations, permanently transforming e-commerce.

The post How Is AI Revolutionizing the Online Shopping Experience? first appeared on AITechTrend.

The researchers built a dataset called CodeOcean with 20,000 examples of instructions for four types of code-related tasks: Code Summarization, Code Generation, Code Translation, and Code Repair. Their aim is to boost the performance of Code Language Models (LLMs) through a process called instruction tuning. They also introduced a fine-tuned model called WaveCoder, designed to enhance instruction tuning for Code LLMs, showing better generalization across various code-related tasks compared to other similar models.

WaveCoder is based on recent advancements in Large Language Models (LLMs), highlighting the potential of instruction tuning in improving model capabilities for different tasks. Instruction tuning has proven effective in enhancing the generalization abilities of LLMs in various studies. The researchers introduced the concept of alignment, where pre-trained models learn from self-supervised tasks to better understand text inputs. Instruction tuning allows these models to extract more information from instructions and improve their interaction with users.

Compared to existing methods like self-instruct and evol-instruct, which rely on teacher models and may produce duplicate data, the proposed LLM Generator-Discriminator framework in CodeOcean controls data quality by using source code. It generates more realistic instruction data by taking raw code as input and selecting a core dataset while adjusting data diversity through raw code distribution adjustments.

The study classified instruction instances into four code-related tasks, refined the instruction data to create CodeOcean, and introduced WaveCoder models fine-tuned with CodeOcean. These models demonstrated superior generalization abilities and efficiency in code generation tasks compared to other open-source models. WaveCoder consistently outperformed other models on various benchmarks, emphasizing the importance of data quality and diversity in the instruction-tuning process. The research highlights CodeOcean’s superiority in refining instruction data and improving the instruction-following ability of base models compared to the CodeAlpaca dataset.

In conclusion, the research introduces CodeOcean, a multi-task instruction data approach, and WaveCoder models to enhance the generalization ability of Code LLMs. The proposed LLM Generator-Discriminator framework is effective in generating realistic, diverse instruction data, contributing to improved performance across various code-related tasks. Future work may explore the interplay among different tasks and larger datasets to further enhance performance and generalization abilities.

The post Cracking the Code: Boosting Performance with CodeOcean and WaveCoder first appeared on AITechTrend.

Whether it’s converting hand-written notes, quoting from printed materials, or extracting text from scanned documents, I often require an image to text converter. For all of these conversions, Imagestotext.io is my ideal tool.

It is user-friendly, free, and has all the features necessary for image to text conversion. Keep reading to discover more about this tool in this detailed review. 

Overview Imagestotext.io?

Imagestotext.io is an AI-powered tool that utilizes OCR (Optical Character Recognition) technology. 

This technology helps users convert hand-written, human-typed, or scanned documents and images into text.

It is capable of recognizing and copying the image into editable text. This has transformed the way people extract text from pictures or documents. It not only replaces manual transcription but also eliminates the chances of human error in text extraction.

How to Use Imagestotext.io?

As I said earlier, this picture to text converter is the simplest tool to use. It requires no signup and just 2 steps to convert an image into text. 

1. Upload the image (supportive formats are PNG, JPG, GIF, JPEG)

2. Click on the button “Get Text”, and copy the extracted text.

Remarkable Features of Imagestotext.io

It is one of the many tools available for online image-to-text conversion. However, the accuracy level and efficiency can vary greatly among these tools, with some being more advanced and efficient than others.

It features integrated and updated OCR technology, which enables it to provide quick and accurate text recognition and extraction. Here are some of the remarkable features of this AI converter tool:

• Latest OCR Technology

This image to text converter features the latest OCR models, enabling the tool to copy text from images with maximum accuracy. 

It allows for better text recognition, supports various image formats, and automates picture-to-text conversion. All of these features make the tool valuable for users in diverse professions.

• Multilingual OCR Models 

Multilingual OCR models in an image-to-text converter enable it to recognize text in images in different languages. This feature not only saves users’ time but also enhances data accessibility among users from various regions.

One major benefit of this feature is that it breaks down language barriers in our increasingly globalized world, where individuals and businesses interact across linguistic boundaries.

• Batch Processing 

As its title indicates, batch processing by the picture to text converter allows content extraction from multiple images at a time.

The user can upload up to 5 images (less than 100MB) per attempt. This feature helps compile content and save the user’s time and energy.

• Accepts All Image Formats 

This advanced jpg to text online accepts 4 different most commonly used image formats including:

• PNG
• JPG
• GIF
• JPEG

• Recognize Distracted Text and Distorted Fonts

Photo to text converter online is capable of reading distracted text and diverse fonts. This feature of the tool is a blessing, especially for copying hand-written documents. 

Diverse font recognizing capability is helpful for users as it ensures that nothing is lost during image to text conversion. It is also helpful in text extraction from different sources. 

• Scalable Text Extraction 

Scalable text extraction means the picture to text converter can handle both small and large-sized images (less than or up to 100MB). 

It eliminates the need for frequent tool replacement by allowing diverse text extraction tasks through a single platform.

Price Plan of Imagestotext.io

It is a free online photo to text converter online. It supports multiple image formats, and users can upload up to 5 images at a time for combined text extraction.

Pros and Cons of Imagestotext.io

Like any other tool or technology, this AI image to text converter also has some limitations along with many other useful and incomparable advantages. 

Pros:

• User-friendly Interface 

There are no formalities required to extract text from images. Moroever, it is just a 2-steps process).

• The best alternative to manual writing

It eliminates the need to copy text from images manually. Instead, the user just has to upload the image and click on the “get text” button for text conversion. 

• No risk of human error 

Digitizing the process of extracting text from images eliminates the chances of human errors that may occur during manual typing or writing.

• Digital data storage:

There is no need to store information or data on paper when you have an image to text converter. Storing data on digital devices also allows for better searchability.

• Free of cost 

Some pictures to text converters are paid to unlock all the features, however, this AI software is completely free to use and allows unlimited conversions.  

• Editable text extraction 

The extracted text can be copied and edited at any time. 

• Recognize different languages 

Photo to text converter online is capable of recognizing and extracting text in English, Arabic, Spanish, French, etc. languages.  

• Accepts 4 image formats

Images in PNG, JPG, GIF, and JPEG formats are acceptable. 

• Queue Processing

The tool allows conversion of up to 5 images less than 100MB into text at a time. 

Cons:

• Requires internet connectivity 
• Destroy the original text formatting 

Conclusion 

As I conclude today’s review of Imagestotext.io, I want to assure you that this AI conversion tool is the most efficient and accurate in its functionality.

Its simple-to-use design and advanced software are the reasons for its exceptional performance. Multilingual support and compatibility with various image formats are additional benefits of this image to text converter.

If you are a writer, journalist, or content marketer in search of the most efficient yet simple image to text converter, I am confident in saying that this one is the best choice for you.

The post Imagestotext.io: (Features, Price, Pros and Cons) – The In-depth Review first appeared on AITechTrend.

1. Vision Beyond Boundaries: Bionic Eyes on the Horizon

In the world of technological marvels, a trailblazing trend is taking shape – one that traces its roots back to Alastor “Mad-Eye” Moody from the realm of Harry Potter. What once seemed like fiction is now inching closer to reality. Researchers are steadfastly working towards achieving the pinnacle of visual innovation – bionic eyes. As early as January 2021, a groundbreaking surgery took place in Israel where surgeons implanted the world’s very first artificial cornea into the eyes of a 78-year-old gentleman robbed of his sight. The outcome was nothing short of awe-inspiring: the patient could once again relish the joys of reading and recognize the faces of his loved ones. This astonishing feat was made possible by an implant capable of seamlessly fusing with human ocular tissue, a testament to the fusion of biology and technology.

2. Soaring Above Congestion: Airports for Flying Taxis

Cast your mind back to the black-and-white era, a time when the concept of flying cars ignited imaginations. However, skeptics argued that technology wasn’t yet ready to lift such ambitions off the ground. In the present day, however, urban landscapes are grappling with congestion, and the need for innovative mobility solutions has never been more pressing. Enter the era of flying taxis and delivery drones, poised to redefine urban transport. The very first Urban AirPort, supported by the UK government, is now a tangible stepping stone toward the future of transportation. Propelled by the power of hydrogen generators, these vehicles promise to usher in a new era of clean and efficient mobility, poised to alleviate congestion and revolutionize urban travel.

3. Bricks Beyond Building: Energizing Structures with Innovation

In a world replete with smart devices, a new chapter is being written in the form of smart bricks – a paradigm shift that extends beyond conventional electronics. Pioneering minds have discovered a novel means of harnessing energy within the very materials that shape our homes. Researchers from Washington University in Missouri have conceived a revolutionary brick that doubles as a battery, weaving energy storage seamlessly into the fabric of our dwellings. Though its journey is in its infancy, the potential is staggering. Picture a future where these unassuming bricks hold the capacity to store significant amounts of energy, contributing to a sustainable and resilient energy landscape.

4. Empowering Wearables: Harnessing Sweat for Innovation

For fitness aficionados and those inclined to embrace convenience, a groundbreaking innovation is afoot in the realm of wearables. Engineers at the University of Glasgow have ushered in a new era of smart wearables that derive energy from sweat – an innovation that transcends the limitations of conventional batteries. This ingenious creation sidesteps traditional power sources, instead harnessing the power of sweat to energize its functionalities. A mere 20 microliters of fluid can fully charge this cutting-edge wearable, with the device ingeniously integrating sweat transport mechanisms into its design.

5. The Dawn of Techno-Organic Hybrids: Living Robots

Venturing into a domain where Hollywood’s cautionary tales converge with scientific breakthroughs, the realm of living robots is emerging as a beacon of technological advancement. Anchored in the fusion of stem cells from frog embryos, these minuscule hybrid robots carry monumental implications. Imagine a future where these cellular marvels navigate human bodies for medical interventions or ply the depths of our oceans, combating the menace of microplastics. What seemed like science fiction is now poised to rewrite our understanding of biological integration with technology.

6. Attire Beyond Fashion: Revolutionizing Heart Monitoring

While heart-monitoring wearables have etched a place in modern lifestyles, their accuracy has long been a point of contention. Enter the realm of innovation as KYMIRA engineers embark on crafting a smart t-shirt infused with a single-lead ECG. This remarkable garment has the potential to accurately measure heartbeats and seamlessly transmit the data to the cloud via Bluetooth. Algorithms poised on the cutting edge will analyze this influx of information, swiftly identifying irregularities in heart rates and duly alerting the wearer. The marriage of fashion and function has never been more promising.

7. Speeding Toward Sustainable Mobility: Super-Fast Charging Car Batteries

The evolution of electric vehicles has ignited a quest for rapid-charging solutions, and researchers at Penn State University are leading the charge in this endeavor. Overcoming the limitations of rapid lithium-ion battery degradation, these visionary minds have introduced a groundbreaking approach – super-fast charging car batteries. By subjecting these batteries to controlled heating and cooling cycles, their longevity is significantly enhanced. As the self-heating nickel foil and intelligent cooling systems collaborate within the vehicle’s architecture, a new era of sustainable mobility comes to life.

8. Bridging Minds and Machines: Silicon Chips Redefined

In the sphere of neural connections and artificial intelligence, a revolutionary leap is witnessed through the melding of silicon chips and artificial neurons. This paradigm-shifting advancement enables silicon chips to replicate the electrical properties of real neurons, unlocking a previously unattainable depth of understanding. Professor Alain Nogaret and his team have pioneered an innovative approach, effectively opening the “black box” of neural behavior. With applications ranging from treating heart ailments to combating Alzheimer’s, this fusion of biology and technology heralds a new era of medical possibilities.

9. Harmonizing with Nature: Sonic Solutions for Forest Fires

The menace of forest fires looms as a critical threat to both humanity and the environment. Conventional methods of fire suppression often span weeks, even months, leaving devastation in their wake. Enter the realm of sound technology as researchers at George Mason University explore the potential of sonic extinguishers. By harnessing the power of sound waves – pressure oscillations to be precise – a unique approach emerges. These pressure waves possess the potential to disrupt the vital supply of oxygen to the flames, curtailing their voracious advance and bestowing upon firefighters a powerful new tool in their battle against infernos.

10. Structures That Heal: The Living Concrete Revolution

In a world striving for sustainable solutions, the innovative fusion of sand, gel, and bacteria has birthed a revolutionary material – living concrete. Boasting both load-bearing strength and intrinsic self-healing capabilities, this marvel of engineering and biology holds the potential to reshape our approach to construction. A monumental need for eco-conscious alternatives to traditional concrete has paved the way for this innovation, a material with the capacity to redefine the very foundations of our built environment.

The post Revolutionizing the Future: 10 Innovations That Will Redefine Tomorrow first appeared on AITechTrend.

In the field of machine learning, decision trees are a popular algorithm due to their simplicity and interpretability. They mimic human decision-making processes by creating a tree-like model of decisions and their possible consequences. The accuracy of a decision tree heavily depends on how well the data is split at each node. Information Gain is a commonly used measure to evaluate the quality of a split.

What is a Decision Tree?

A decision tree is a flowchart-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome or the value of the target variable. The goal of a decision tree is to create a model that predicts the value of a target variable based on several input features.

Understanding Information Gain

Information Gain is a measure that quantifies the amount of information obtained about the target variable by splitting the data based on a particular feature. It calculates the reduction in entropy or impurity in the target variable after the split. A high information gain indicates that the split is effective in separating the data into distinct classes or categories.

How Does Information Gain Work?

To understand how Information Gain works, we need to first understand the concept of entropy. Entropy is a measure of impurity or disorder in a set of examples. In the context of decision trees, entropy represents the amount of uncertainty in the target variable. The goal is to minimize the entropy, which means maximizing the information gain.

Calculating Information Gain

The formula for calculating Information Gain is as follows:

Information Gain = Entropy(parent) - Weighted Average Entropy(children)

Here, the entropy of the parent node is subtracted from the weighted average entropy of the child nodes. The weighted average takes into account the proportion of examples in each child node relative to the parent node.

Example of Information Gain Calculation

Let’s consider an example to illustrate how Information Gain is calculated. Suppose we have a dataset of emails labeled as “spam” or “not spam” based on certain features. We want to determine the best feature to split the data. We calculate the entropy of the parent node, then calculate the entropy of each child node after the split. Finally, we calculate the Information Gain for each feature and choose the one with the highest value.

Advantages of Information Gain

Information Gain has several advantages that make it a popular choice for decision tree splitting. First, it is easy to understand and interpret. Second, it can handle both categorical and numerical features. Third, it is computationally efficient and can be calculated relatively quickly. Lastly, it tends to create balanced splits, which can lead to more robust decision trees.

Limitations of Information Gain

While Information Gain is a widely used splitting criterion, it also has certain limitations. One limitation is its bias towards features with a large number of categories. Features with high cardinality tend to have higher information gains simply due to the larger number of possible splits. Another limitation is its inability to handle missing data effectively. Decision trees using Information Gain may struggle when confronted with missing values in the dataset.

Choosing the Best Splitting Criterion

Although Information Gain is a popular choice, it’s important to note that there are other splitting criteria available, such as Gini Index and Chi-Square. The best splitting criterion depends on the nature of the problem and the characteristics of the dataset. It’s recommended to experiment with different criteria and evaluate their performance to choose the most suitable one.

Conclusion

Information Gain is a valuable measure for determining the quality of splits in decision trees. By quantifying the reduction in entropy, it helps identify features that effectively separate the data and improve the accuracy of the resulting decision tree. However, it’s essential to consider the limitations and explore alternative splitting criteria to ensure optimal performance.

The post The Ultimate Guide to Decision Tree Splitting: Harnessing Information Gain for Optimal Results first appeared on AITechTrend.

In this article I wanted to share some of the latest AI trends that are revolutionising technology and industries across the globe. Get ready to be amazed by the power of AI!

AI for Everyone

In recent years, we’ve witnessed a remarkable shift in the world of artificial intelligence (AI) – what was once an exclusive domain of tech giants and experts has now become accessible to the masses. This phenomenon is known as the “great democratisation” of AI, and it’s changing the game for businesses and individuals alike.

One of the most significant examples of this democratisation is the rise of AI-powered chatbots. AI chatbots like ChatGPT are revolutionizing the world of AI for everyone by making it easier for people to interact with AI technology. With the rise of chatbots, individuals and businesses can easily and quickly integrate AI-powered solutions into their daily lives. Chatbots are used for a wide range of purposes, such as customer service, sales, marketing, and even mental health counseling. They can handle repetitive tasks and provide quick, accurate responses to a variety of queries, freeing up human resources for more complex and creative work. As AI technology continues to advance, chatbots are becoming increasingly sophisticated, and their potential applications are virtually limitless.

But that’s not all. Predictive analytics is another powerful tool that has become increasingly accessible to businesses of all sizes. By analysing data from various sources, companies can now optimise their operations and provide personalised experiences to their customers like never before. This means businesses can offer tailored products, services, and marketing campaigns to individual customers, improving their overall experience and satisfaction.

The democratisation of AI has also made it more affordable for smaller businesses to compete with their larger counterparts. With AI-powered tools, they can automate tasks, streamline processes, and make data-driven decisions, giving them an edge in the market.

Creativity Unleashed

Generative AI has taken AI to new heights by creating entirely new content rather than just processing existing data. With applications like generative art, music, and language models like GPT-4, AI is fostering creativity and innovation in ways we’ve never seen before.

Having an AI system that can write catchy marketing content or compose music tailored to your unique taste is no longer a far-off dream. With generative AI, the possibilities are endless, and we’re just scratching the surface of what’s possible.

Trustworthy and Transparent

As AI continues to play a more significant role in our lives, ensuring that these systems are ethical, transparent, and accountable is more important than ever. That’s where ethical and explainable AI come in.

An ethical AI system is one that can justify its decisions and actions, ensuring fairness and trust. For example, an AI hiring tool should be transparent in its decision-making process and avoid perpetuating discriminatory practices.

Explainable AI, on the other hand, ensures that the decision-making process of an AI system can be understood by humans. This is crucial in avoiding biases and ensuring that these systems are not making decisions based on factors that could be considered unfair or discriminatory.

By prioritising ethical and explainable AI, we can ensure that these systems are being used responsibly and that everyone is treated fairly. It’s an essential step in building trust between humans and AI, and it’s exciting to see the progress being made in this area.

Enhancing Human Work

This heavily depends on the school of thought you subscribe to, but my belief is that AI is not here to replace humans. Instead, it’s here to enhance our work and make our jobs easier. Augmented working is the use of AI-powered tools to automate repetitive tasks, provide real-time insights, and assist with decision-making.

The benefits of augmented working are numerous. For example, in healthcare, AI-assisted diagnostics are helping doctors to make more accurate diagnoses and treatment plans. In finance, AI is assisting with investment decision-making by providing real-time insights into market trends and risks. By using AI to enhance our work, we’re increasing efficiency, accuracy, and job satisfaction across industries.

Additionally, augmented working is freeing up valuable time and resources that can be better spent on more complex and creative tasks. By automating repetitive tasks, we’re able to focus our attention on higher-level decision-making and problem-solving, which can lead to innovation and new discoveries.

AI for a Greener and Fairer World

Sustainable AI is all about creating AI systems that are environmentally and socially responsible. AI-powered energy management systems, waste reduction algorithms, and ethical supply chain monitoring tools like those offered by Permutable AI are transforming industries. AI can optimise energy consumption, reduce waste, and promote ethical practices in global supply chains, leading to a greener and more equitable world.

Speed and Efficiency

Edge Computing for AI Processing Edge computing enables faster and more efficient AI processing by handling data close to its source instead of relying on centralised data centres. Examples include autonomous vehicles, IoT devices, and smart city applications. Edge computing for AI provides numerous benefits, including reduced latency, increased privacy, and lower bandwidth requirements. For instance, autonomous vehicles can make quicker decisions with locally processed data, ensuring safer roads.

AI in Action

Transforming Industries From healthcare to education, AI is revolutionising various sectors:

Healthcare: AI-powered tools can analyse medical images, patient data, and genomics to diagnose diseases early and recommend personalised treatment plans, improving patient outcomes.

Finance: AI is enhancing fraud detection, risk management, and investment decision-making, boosting efficiency and security in the industry.

Customer Service and Communication: Advancements in natural language processing (NLP) and chatbots have improved customer experiences and reduced operational costs for businesses.

Transportation and Logistics: AI-powered autonomous vehicles are transforming goods transportation, optimising routes, predicting maintenance needs, and managing fleets more efficiently.

Education: AI applications in education, such as personalised learning and adaptive assessments, enhance academic outcomes and increase student engagement.

AI for Social Good

AI also plays a significant role in addressing pressing social issues. For example, AI algorithms can analyse satellite imagery to track deforestation, illegal fishing, and other environmental issues. AI-powered tools can also help monitor and predict natural disasters, such as hurricanes, earthquakes, and wildfires, enabling faster and more effective responses. In the fight against global challenges like climate change, poverty, and inequality, AI has the potential to make a meaningful impact and improve countless lives.

Preparing for an AI-Driven Future

As AI continues to advance and reshape the world around us, it’s essential to stay informed about the latest AI trends and their potential implications. By democratising AI, fostering creativity with generative AI, ensuring ethical and sustainable practices, and augmenting human work, these trends showcase AI’s incredible potential to positively impact our lives and the world at large.

As we look ahead, the possibilities for AI are limitless. New AI trends and applications will continue to emerge rapidly, reshaping industries, and enhancing our lives in ways we can’t even imagine. However, as AI becomes increasingly ingrained in our society, we must address the challenges it poses.

Some of the key challenges we face include data privacy concerns, ethical concerns, the potential for job displacement, and addressing biases in AI algorithms. By actively engaging in conversations and collaboration, we can work together to create a future where AI is used responsibly and ethically, ensuring that its benefits are shared by all.

The post Latest AI Trends: Exploring the Future of Artificial Intelligence Technology and News for Tech Readers first appeared on AITechTrend.

TensorFlow, an open-source machine learning framework, has revolutionized the field of deep learning by providing a powerful platform for building and training neural networks. One of the key factors that contribute to the success of TensorFlow is its ability to optimize and accelerate the execution of models. In this article, we will explore how TensorFlow models can be further accelerated using the XLA (Accelerated Linear Algebra) compiler.

Understanding the XLA compiler

What is the XLA compiler?

The XLA compiler is a key component of TensorFlow that aims to improve the performance and efficiency of executing TensorFlow models. It achieves this by just-in-time (JIT) compiling TensorFlow computation graphs into highly optimized machine code, specifically tailored for the underlying hardware architecture.

How does the XLA compiler work?

The XLA compiler takes the TensorFlow computation graph as input and performs various optimizations to generate efficient machine code. It analyzes the graph’s operations and identifies opportunities for fusion, where multiple operations can be combined and executed together. Additionally, the XLA compiler applies optimizations such as constant folding, loop unrolling, and memory layout transformations to further enhance the performance of the compiled code.

Benefits of using the XLA compiler

Improved performance and efficiency

One of the primary benefits of using the XLA compiler is the significant improvement in performance and efficiency. By optimizing the computation graph and generating highly optimized machine code, the XLA compiler can leverage the full potential of the underlying hardware, resulting in faster execution times and reduced resource utilization.

Simplified code

Another advantage of the XLA compiler is the ability to simplify the code. The XLA compiler optimizes the TensorFlow operations and automatically handles various low-level details, such as memory management and parallelization. This allows developers to focus on the high-level logic of their models without worrying about the intricacies of hardware-specific optimizations.

Seamless integration with TensorFlow

The XLA compiler seamlessly integrates with TensorFlow, making it easy to incorporate into existing TensorFlow workflows. It can be used with both the eager execution mode and the graph mode, providing flexibility and compatibility with different TensorFlow versions.

Getting started with the XLA compiler

Installing TensorFlow with XLA support

To start using the XLA compiler, you need to install TensorFlow with XLA support. You can do this by following the official TensorFlow installation instructions provided by the TensorFlow website. Make sure to select the appropriate version of TensorFlow that includes XLA support.

Enabling XLA in TensorFlow

Once you have TensorFlow installed, you can enable XLA by adding a few lines of code to your TensorFlow script. By default, XLA is disabled, so you need to explicitly enable it. Here’s an example of how to enable XLA in TensorFlow:

import tensorflow as tf

# Enable XLA
tf.config.optimizer.set_jit(True)

# Rest of your TensorFlow code...

With these lines of code, XLA will be activated, and TensorFlow will leverage the XLA compiler for optimizing your computation graph.

Compiling TensorFlow models with the XLA compiler

Optimizing TensorFlow operations with XLA

The XLA compiler can optimize various TensorFlow operations, including matrix multiplications, convolutions, and element-wise operations. By fusing multiple operations together and applying optimization techniques, the XLA compiler can reduce the overhead and improve the overall performance of your TensorFlow models.

XLA compilation options and parameters

The XLA compiler provides several compilation options and parameters that allow you to fine-tune the optimization process. These options include controlling the level of optimization, enabling or disabling specific optimizations, and specifying target hardware architectures. By experimenting with different options, you can find the optimal configuration for your specific use case.

Accelerating TensorFlow models with the XLA compiler

Leveraging XLA for faster inference

One of the primary use cases for the XLA compiler is accelerating the inference phase of TensorFlow models. By compiling the computation graph with XLA, you can achieve faster and more efficient predictions. This is particularly beneficial in scenarios where real-time or near-real-time inference is required, such as in production environments or resource-constrained devices.

Boosting training speed with XLA

The XLA compiler can also contribute to faster training of TensorFlow models. By optimizing the computation graph and generating highly efficient machine code, XLA reduces the computational overhead during the training process. This can lead to shorter training times and faster convergence, enabling researchers and practitioners to iterate on their models more quickly.

Best practices for using the XLA compiler

Profile and tune your code

To get the most out of the XLA compiler, it is essential to profile and tune your TensorFlow code. Use profiling tools provided by TensorFlow to identify performance bottlenecks and areas where XLA optimization can be beneficial. By understanding the specific characteristics of your models and data, you can make informed decisions on how to optimize and utilize the XLA compiler effectively.

Take advantage of XLA’s autotuning capabilities

The XLA compiler features autotuning capabilities, which automatically optimize the compilation process based on the target hardware architecture and the characteristics of your TensorFlow models. This means that even without manual intervention, XLA can adapt its optimizations to achieve the best performance for your specific hardware setup.

Using XLA with custom TensorFlow operations

The XLA compiler is compatible with most standard TensorFlow operations. However, if you have custom operations or specialized layers in your models, you need to ensure that they are XLA-compatible. Consult the TensorFlow documentation and guidelines for creating XLA-compatible operations to ensure seamless integration with the XLA compiler.

Limitations and considerations

Compatibility with different hardware architectures

The XLA compiler’s optimizations are tailored for specific hardware architectures, such as CPUs, GPUs, and TPUs. While it provides excellent performance on supported architectures, it may not be fully compatible with all hardware configurations. It is essential to ensure that your target hardware is compatible with the XLA compiler to take advantage of its optimization capabilities.

Handling memory constraints

The XLA compiler optimizes memory usage to improve performance, but it’s important to consider memory constraints when using the XLA compiler. Depending on the size of your models and the available memory on your hardware, you may need to adjust the batch sizes or employ other memory optimization techniques to avoid out-of-memory errors.

Real-world examples and success stories

Applications and industries benefiting from XLA

The XLA compiler has been successfully used in various applications and industries. It has shown remarkable performance improvements in areas such as computer vision, natural language processing, recommendation systems, and speech recognition. Industries including healthcare, finance, e-commerce, and autonomous vehicles have leveraged the power of the XLA compiler to accelerate their TensorFlow models and achieve better results.

Case studies of accelerated TensorFlow models

Numerous case studies highlight the effectiveness of the XLA compiler in accelerating TensorFlow models. For example, a research team in the field of medical imaging used XLA to optimize their deep learning models, resulting in significant speed-ups during both training and inference. Another case study involved a recommendation system in the e-commerce industry, where XLA accelerated the model’s predictions, leading to faster and more personalized recommendations for customers.

Future developments and advancements

Ongoing research and improvements for XLA

The XLA compiler is an active area of research and development within the TensorFlow community. Ongoing efforts focus on further improving the optimization techniques, expanding hardware compatibility, and enhancing the integration with other deep learning frameworks. TensorFlow developers and researchers are continuously working on advancements to ensure that the XLA compiler remains at the forefront of accelerating TensorFlow models.

Integration with other deep learning frameworks

While the XLA compiler is primarily associated with TensorFlow, there is a growing interest in integrating it with other deep learning frameworks. The goal is to extend the benefits of XLA’s optimization capabilities to a wider range of frameworks, allowing developers to leverage its power regardless of the deep learning platform they choose.

Conclusion

The XLA compiler provides a powerful tool for accelerating TensorFlow models by optimizing computation graphs and generating highly efficient machine code. By leveraging the XLA compiler, developers and researchers can achieve significant performance improvements and reduce resource utilization. With its seamless integration into TensorFlow and ongoing advancements, the XLA compiler continues to push the boundaries of deep learning acceleration.

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It’s the application of artificial intelligence, machine learning, deep learning, and big data to manage, automate and improve IT operations.

The latest Gartner report on AIOps reveals that AIOps adoption is increasing across organizations. Organizations are adopting AIOps with different maturity levels across these domains and are increasingly being adopted across application operations, infrastructure operations, Cloud Management, DevOps, Security.

A quick view of the AIOps Maturity and Adoption model below will give you a view of the use-cases being adopted across multiple maturity levels in different organizations.

Organizations have adopted a pragmatic approach and gained initial success in adopting intelligent Alerting for full-stack monitoring, co-relation, and Automated ticket resolution for service management. As referred to in the above maturity model many organizations are already reaping benefits by early adoption of use-cases up to Level 3 of the AIOPS maturity model.

How do organizations move to the next level of AIOPS Adoption and what are the adoption challenges?

Data Siloes don’t help.

Just IT-centric data is not enough… it is critical to bring data from business as well, we need to bring data from DevOps, cloud management, Security as well. for eg: for telecom customers, data from the BSS/OSS systems is critical. For a retailer, data from the supply chain, POS, and warehouse systems are critical. It is important to break these data siloes. Organizations need to enable distributed data and data mesh architectures and enable leveraging existing Data domains for the same. AIOps can be enabled based on co-relating existing data domains and generate new Insights.

Organizations are yet to unleash the power of Data and AI.

It is important to leverage all data small or big to come with Rich AI-based Insights. Mostly the power of algorithms becomes richer with more and more data being leveraged for decision making. Heuristic-based approaches enable us to get the system working quickly, however, the power of data for decision making should be a structured and ongoing effort. Also, AI engineering is not fully operational for many of these systems and hence the power of Data and AI is not fully realized. It is important to put proper AI engineering frameworks and practices in place for Data Operations, Model Operations to enable accurate algorithmic-based decision making.

User Experiences

The single pane of Glass as the silver bullet for all problems AIOps intends to solve has certainly allowed initial adoption. The truth for the users we have not yet allowed the users to be free from the drudgery of data and dashboards. Unless we provide them personalized insights and enable decision-making, users will find it difficult to bite the Silver Bullet. Also, users are increasingly working remotely and with hybrid work becoming the norm, user experience is very critical to higher adoption of AIOps.

IT systems don’t work in isolation. They are part of a complex business landscape driving the digital experiences for customers, employees, and partners. While the power of data can drive Insights, it is the understanding of the business process and application landscape, its impact, and the historical context that helps navigate AIOps enabling complex decisions for businesses for eg: Change Impact analysis or a causal analysis and recommendations. This is also called Observability. Existing tools or frameworks allow us to build this partially because of the operational siloes… Building this context enables us to enable the right decision-making for AIOps thus enabling higher business benefits.

The Holy Grail of AIOps is not right here and right now, but it involves a structured approach to solve these challenges through a coherent enterprise-wide AIOps approach.

How would your enterprise approach this? Would like to know your ideas and suggestions…​

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By now you know that no matter how much property you’ve acquired, there’s always something you’ll need soon enough. Aside from basic items like food, water, clothing, and shelter, the need to acquire things we don’t have is human nature. 

So how do you acquire things you don’t have using what you have? The answer is simple — commerce (buying and selling). We buy what we need using what we have, or the monetary equivalent of what we have. 

Commerce at its core was built on consumer products (things you can use directly). The early men traded basic items like food and clothing using the barter system. Today, we still buy the same class of items using money.

Do you still think it’s hype? 

Humans had always craved convenience where available. The rise of the internet has provided new ways of doing old things, thus making life easier.

Basically, any commercial activity done over the internet is e-commerce.

E-commerce makes it easy for us to meet one of our basic needs. The desire to buy items conveniently, know the prices of items when budgeting, among others, has made e-commerce a better option for regular commerce. 

The retail industry, on the other hand, has seen a dramatic shift in the means of delivery of customers’ goods. In the early 1900s, big players like Walmart and Macy’s used departmental stores to give customers the best shopping experience.

Today, industry Titan – Amazon is leading the wave of e-commerce using the internet and fulfillment centers to give customers the 21st-century buying experience — shopping convenience.

The online retail marketplace is a cool place to shop, but many users complain that long delivery dates are a pain point. Amazon’s 2-hour delivery for prime users is a close shot at addressing the problem.

Gorillas, an on-demand grocery delivery company in Europe with the promise of delivering groceries within 10 minutes of ordering, is trying to address the issue of the prolonged goods delivery.

In Nigeria, a startup like Phlenyx is going to address the same issue, thus providing up to 90 minutes of goods delivery after its beta launch in December 2021. 

So why is there so much buzz about e-commerce?

The answer is simple — e-commerce is the future of retail trading, and we are living in that future. Buying from supermarkets and “mom-and-pop” stores won’t be going away soon, after all, over 95% of global retail trading is done offline. Nevertheless, the transition to retail e-commerce is quite rapid, with a 27.6% global annual growth in 2020 compared to 20.2% in 2019.

E-commerce is here to stay. As customers increase their expectations, retailers have to come up with innovative solutions to meet the demand. With this in mind, we hope to see more e-commerce innovations in the few years to come.

Ever since Amazon established itself as the go-to online retail marketplace, other retailers — big and small — had followed suit. The internet has created an easy-to-access stream of the audience, thus making it very easy for businesses to have direct connections with their customers. 

Shopify, a subscription-based software that allows anyone to set up an online store and sell their products, is helping small and medium scale retailers to take advantage of the e-commerce wave. Today, over 1.75 million merchants operate their online stores using Shopify.

The current supply chain of consumer products makes it hard for producers to get direct feedback from customers. Here’s how the standard supply chain works:

• The producer sells to the distributors
• The distributors sell to the wholesalers
• The wholesalers sell to the retailers 
• The retailers sell to the consumers

The above process is cumbersome and hard to keep track of. By using e-commerce, businesses and retailers alike can sell directly to consumers, thereby cutting the middlemen.

Direct to customer (D2C) sales aren’t limited to cosmetics and fashion as you may think. Tesla, an electric vehicle and clean energy company doesn’t use dealers to sell their cars, they sell directly to you. You can easily order a Tesla EV car on their website and have it delivered to your doorstep. 

Some of the advantages of D2C sales include direct access to customers feedback and higher profit margins. A key disadvantage of using this approach is that it is quite expensive to establish a logistics network to meet customers’ demands. Companies who use the model must make a hefty profit margin per item sold to cover the added operational cost from making low-cost deliveries to consumers. 

In summary, D2C sales remove distributors and wholesalers out of the picture, thereby leaving just two key players in the retail industry — producers and consumers.

Should this be the norm for all retail categories? 

Author Bio

Bethel Udochukwu CEO and Co-founder of Phlenyx. Product designer (UI/UX), data analyst, and AI enthusiast with a passion for innovative technology especially in e-commerce.

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