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carnbikecafe · 1 month ago

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Upcoming Bikes in January 2025

As the calendar flips to a new year, bike enthusiasts eagerly await the launch of new models that promise to revolutionize the two-wheeler industry. January 2025 is set to bring a slew of exciting motorcycles catering to various segments, from performance-oriented machines to commuter-friendly options. Here’s a comprehensive look at the upcoming bikes to watch out for this month.

1. Yamaha R15 V5

The Yamaha R15 has been a favorite among sportbike lovers, and the upcoming R15 V5 aims to take the legacy forward. Expected upgrades include:

Improved aerodynamics.

A re-tuned 155cc engine with better low-end torque.

Enhanced digital instrumentation with smartphone connectivity.

Approximate Price: INR 1.85 lakh (ex-showroom).

2. Royal Enfield Himalayan 450

Royal Enfield enthusiasts have been eagerly awaiting the Himalayan 450, touted as a game-changer in the adventure touring segment. Key features:

A brand-new 450cc liquid-cooled engine.

Advanced rider aids, including switchable ABS and multiple riding modes.

Enhanced ergonomics for better long-distance comfort.

Approximate Price: INR 2.8 lakh (ex-showroom).

3. KTM Duke 490

KTM’s Duke series is synonymous with aggressive styling and performance. The Duke 490 is expected to fill the gap between the Duke 390 and Duke 790. Anticipated highlights:

A twin-cylinder engine delivering around 50 horsepower.

Lightweight trellis frame for improved handling.

TFT display with smartphone integration.

Approximate Price: INR 3.5 lakh (ex-showroom).

4. Hero Xtreme 160R 4V

Hero MotoCorp is all set to up its game with the new Xtreme 160R 4V. This streetfighter will feature:

A 160cc 4-valve engine for better performance.

Sharp and aggressive styling updates.

Enhanced LED lighting and a digital console.

Approximate Price: INR 1.3 lakh (ex-showroom).

5. Bajaj Pulsar NS 400

The Bajaj Pulsar NS series has been a staple for performance enthusiasts, and the NS 400 aims to take it a notch higher. Expected features include:

A 373cc single-cylinder engine derived from the Dominar.

Sporty design with improved suspension setup.

Dual-channel ABS and slipper clutch.

Approximate Price: INR 1.9 lakh (ex-showroom).

6. TVS Zeppelin R

TVS is finally bringing its much-anticipated cruiser, the Zeppelin R, to the market. Key features:

A 220cc hybrid engine with integrated starter-generator.

Belt drive for smoother performance.

Cruiser-style ergonomics with premium build quality.

Approximate Price: INR 1.75 lakh (ex-showroom).

7. Honda CB300F Facelift

Honda plans to refresh its CB300F model with a facelift that promises to improve its appeal. Highlights:

Revised styling with LED headlamps and new color options.

BS6 Phase 2-compliant engine.

Improved ride quality with tweaked suspension settings.

Approximate Price: INR 2.3 lakh (ex-showroom).

8. Suzuki V-Strom 250 SX

Adventure bike lovers can look forward to Suzuki’s V-Strom 250 SX, designed for light off-roading and long-distance touring. Key features:

A 249cc single-cylinder engine.

Semi-fairing design with tall windscreen.

Spoked wheels and dual-purpose tires.

Approximate Price: INR 2.1 lakh (ex-showroom).

January 2025 is shaping up to be an exciting month for motorcycle enthusiasts. With cutting-edge technology, diverse options, and competitive pricing, these upcoming bikes are sure to cater to a wide range of riders. Whether you’re looking for a daily commuter, an adventure machine, or a performance beast, this lineup has something for everyone. Stay tuned for detailed reviews and first looks as these bikes hit the showrooms!

#automobile #bike #cars #car

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khivrajmotors · 4 months ago

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Comparison and analysis New Triumph Speed 400 T4 Triumph Speed 400 MY25.

Introduction

In terms of reach and practicality, Triumph’s Speed 400 series cover some significant ground as motorcycle alternatives that deliver good performance, nice look and with more affordable prices. In this blog post we’re going to explore in detail two of the models that stood out in the Triumph lineup: the Triumph Speed 400 T4 and the Triumph Speed 400 MY25. These two bikes have their specialties and we will give you the details of their features, behavior, and appreciation to assist you to make the correct decision.

Visual Design and Appeal

Speed 400 T4: The Triumph Speed 400T4 shares some of the design cues of some of the most iconic Triumph motorcycles of the past. They include a round headlamp, a tear-drop fuel tank and an almost skeletal tail section, all touched by retro styling. Another main point of the T4 design is to walk that fine line between simplicity and style and therefore the T4 looks perfect if you are that type of a rider who likes to follow all styles but actually does not.

Speed 400 MY25: While other phones in the Speed 400 series have a sober look, the Speed 400 MY25 has a modern and sporty design. When it comes to exterior design, the MY25 has a modern look with nice and clear lines, smooth LED headlamp and sporty look. It is most suitable with riders who have a need for that sexy looking bike that responds perfectly to any riders.

Power and Efficiency

Speed 400 T4: These two models’ engines are similar; both of them use 398cc DOHC 4V single-cylinder liquid cooled but the tuning of the engines differ. The Speed 400 T4 delivers 30.6 bhp at a shaft speed of 7,000 mph and 36 Nm of torque at 5,000 rpm. This kind of a design is perfect for comfort, ease and stability for those who use the bicycle for everyday commuting in the city or for those who ride for recreational purposes.

Speed 400 MY25: The Speed 400 MY25, but it is tailored for more power of up to 40 bhp at 8,000 RPM and 37.5 Nm torque at 6,500 RPM. This makes the MY25 as one that has an increased performance level and is perfect for those riders who consider speed as a crucial element on their travels.

Managing and Riding Excellence

Speed 400 T4: The petrol powered Speed 400 T4 boasts 43mm RSU telescopic front forks and a gas mono-shock RSU equipped with an external reservoir at the rear. This brilliant setup allows a comfortable and stable roof and the suspension system enables the car to successfully handle all sorts of humps and undulations on the road. T4 has had a slightly broader handlebars and higher seat than T3, and therefore riders will experience more relaxed riding postures.

Speed 400 MY25: On the other hand, the Speed 400 MY25 comes with USD forks that measures 43mm and they come in gold making the bike more appealing. Compared to the significantly more comfortable MY25’s suspension, the MY17’s is a little stiffer, particularly at the rear, and offers superior feedback at the limit. The hydraulic levers of the MY25 also bring extra flexibility in riders’ control for their riding preferences.

Price & Value for Money

Speed T4: The Speed T4, an even more performance-focused and with more aggressive looks than the standard T3, relegated the 110 to a higher price bracket. And this is more for the enduro rider seeking a new super exciting challenging and expensive ride! Due to the sophisticated features and sports handling, it is worth paying for a car for the speed enthusiast.

Speed 400 MY25: The Speed 400 MY25 is an affordable Range Rover that produces good measured performance and comfort and reasonable fuel economy for a lower price range. It’s more suitable for cyclists who are not willing to spend much but want a bike capable of performing both the daily urban commuting and occasional leisure cycling adventure.

Conclusion: Which One Should You Choose?

Speed T4 is advised for a dare devil, who wants his car to be fast, turn quickly and be glamorous. It comes with more power, it increases the acceleration and it comes with naked agility that will make it better for the city and for those couples who need the jolt of the adrenalin rush.

Speed 400 MY25 : If you fancy comfortable and unhurried riding with better fuel economy than the Speed 400 MY25 model will suit you with a pinch of reference to the old school. This is quite versatile for anyone looking to have a balance of traceability and durability, good for both daily to work and back as well as racing.

In conclusion, the chosen bike really depends on specific rider habits and characteristics. From where I stand, if you are interested in outright speed, the Speed T4 should not disappoint, then for comfort and efficiency the Speed 400 MY25 scores high on both aspects.

For More information: A Comparison Review New Triumph Speed 400 T4 vs Speed 400 MY25

#Speed 400 T4 Triumph Speed 400 MY25.

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jcmarchi · 4 months ago

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Maximizing AI ROI in the Enterprise

New Post has been published on https://thedigitalinsider.com/maximizing-ai-roi-in-the-enterprise/

Maximizing AI ROI in the Enterprise

As has been the case with numerous technologies before it, artificial intelligence (AI) is being hailed as the next great innovation enterprises simply must use. Ironically, the underlying technology has been around for decades, but with the latest iterations, the hype has reached a fever pitch—outpacing the reality of implementation across the enterprise. Yet, as IT teams face increasing pressure to get on board the IT train, they must balance that enthusiasm with the reality of the bottom line. Different implementations require different levels of investment, meaning they must also yield a different return—often on a different timetable.

The ability to deliver successful AI products depends on numerous factors: specific strategies, planning and execution chosen by business leaders; availability of skilled resources; fit within product roadmap; organizational acceptance of risk; and time management against expected return on investment (ROI).

Balancing these factors is the challenge, but following these three steps can keep organizations on the path toward AI ROI.

Understand the Technology

Many enterprises enter the AI fray believing they are behind but not fully understanding why, how, or even what the technology is. As a result, their first task is distinguishing among different flavors of AI, beginning with precision AI vs. generative AI.

Precision AI is the use of machine learning and deep learning models to improve outcomes. It enables enterprises to automate decision-making processes, creating efficiencies and increasing ROI. Precision AI has matured into an established workhorse technology for enterprises that continues to see significant adoption and is becoming more mainstream by the day.

Generative AI (GenAI) is new and has risen to prominence since OpenAI released ChatGPT in late 2022. Consisting of foundational large language models (LLMs) trained with billions of parameters to generate new semantic text context, GenAI offers significant opportunities for business impact and operational efficiency but it’s early in its adoption lifecycle.

One significant hurdle is the standard for data quality, which is elevated for GenAI applications since low-quality datasets can introduce transparency and ethical issues.

Data reliability begins with designing and implementing workflows; establishing pipelines to perform; abstracting through APIs; curating and democratizing; and processing different data types. Rather than the previous generation of data quality requirements that included the 4Vs (volume, velocity, veracity and variety), AI needs new requirements that include 4Ps: prediction, productivity, precision, and persona at scale.

Prediction: AI algorithms allow the use of statistical analysis to find patterns in the data and identify behaviors to predict and forecast future events by correlating historical data at rest and data streaming to make decisions in real-time.

Productivity: AI enables business process automation, which increases enterprise operational efficiency and productivity, reducing repetitive tasks and freeing up staff time to work on more strategic assignments.

Precision: This metric measures the model results in a way that machine learning models can produce accuracy between acceptable range determined by the use cases. Precision is also calculated as the number of true positives divided by total number of positive predictions.

Persona at scale: This refers to the process of using reliable data such as customer purchase histories, on-site actions, customers’ sentiment analysis for specific products and survey responses. It delivers individualized experiences across demographics.

In addition to data quality, enterprises must consider numerous other factors—both internal and external—when evaluating their AI readiness: governance, compliance alignment, cloud investments, talent, new business operations models, risk management, and leadership commitment.

Organizations must begin by establishing an AI vision that matches their goals and strategic objectives. Buy-in from the C-suite is critical, as AI deployments require significant up-front investment. The CIO must clearly articulate the path to ROI to the entire C-suite—a true test of the CIO in elevating IT from an enabling function to a strategic one.

Next, the organization must align people, processes, and technology. AI requires new skills and certifications such as deep learning models and machine learning, as organizations have traditionally integrated AI into human workflows. However, GenAI reverses the dynamic, but most best practices and responsible use guidelines still include a “human in the loop” component to maintain ethical standards and values.

An AI deployment also demands new business processes for governance and data quality assurance, enabling the data scientists responsible for delivering new AI models to solve complex business problems.

As new AI products are designed, developed, and manufactured for production, enterprises must also remain vigilant of the AI industry’s latest regulatory policies. The European AI act has established best practices for using AI—and consequences for not following those policies. As a result, enterprises have constructed teams to create, evaluate and update efforts around AI regulations.

With enterprises becoming increasingly data-driven, they must develop foundational strategies to protect the data assets enabling them to deliver the best insights through analytics process automation platforms. From there, they can select the AI technologies and new platforms that make the most sense for them.

Define the Business Case

Finally, true return on an AI investment requires selling the benefit to customers, meaning AI readiness requires a new business mindset as the technology is driving transformation for enterprises across industries.

Successful AI product development requires an intimate understanding of industry-specific customer journeys and aligning AI solutions with business objectives. Customer centricity plays a key role in developing new operating models, and modern technologies are used to increase efficiency.

For instance, customers looking for small wins in AI maturity can rely on their software assets and cloud infrastructure to develop new products and solutions. This keeps satisfaction among employees higher and maintains their focus on exceeding customer expectations.

That said, the core of the organization should focus on shortening time-to-market and improving new process management to shorten the product development life cycle and increase the efficiency of delivering new products. For example, a distributed augmented data analytics platform is used to automate the ingestion, curation, democratization, processing, and analytics in real-time—all of which increase productivity and ROI.

Unlock the Full Potential of AI ROI

AI at its core stands for advanced algorithms, data quality, computing power, Infrastructure as Code, governance, responsible AI with ethics to protect data privacy and confidentiality. The essentials of AI application readiness and the challenges of data management require hardness data-driven frameworks, people, process, strategy ethics and technology platforms.

Concurrently, Mckinsey reports that 65% of enterprises are using AI technologies—double the number from last year. It demonstrates momentum, but deployments are still moving slowly from curiosity to real business use cases at scale. GenAI is delivering new breakthroughs, enabling organizations to harness new capabilities through the development of semantic and multi-modal LLMs. It democratizes a full spectrum of AI capabilities, enabling them to generate new revenue streams.

With the right strategy, leadership commitment, and investment in the correct use cases, businesses can gain significant value and drive transformative growth through AI.

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mvishnukumar · 5 months ago

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Difference between Big Data and Machine Learning

Big Data:

Big data refers to extremely large datasets that are often complex, unstructured, and difficult to process using traditional data management tools. The primary goal of big data is to collect, store, and analyze data to extract useful information. It deals with techniques for handling, processing, and analyzing massive datasets efficiently using technologies like Hadoop, Apache Spark, and distributed computing. The focus is on volume, variety, velocity, and veracity (the 4Vs of big data).

Machine Learning:

Machine learning (ML) is a subset of artificial intelligence that focuses on creating algorithms that allow computers to learn from data. The goal is to identify patterns in the data and use them to make predictions or decisions without explicit programming for each task. It requires various mathematical, statistical, and algorithmic techniques. ML models rely on data for training, and once trained, they can make predictions on new data. Popular ML libraries include TensorFlow, PyTorch, and Scikit-learn.

Programming Skills:

While both require programming, machine learning usually demands a deeper understanding of algorithms, mathematical concepts, and coding for tasks like model building, training, and optimization. Big data typically requires knowledge of data pipelines, data storage, and analytics, often relying on tools that automate much of the process, meaning it might require less hands-on coding but a stronger understanding of the data architecture.

#data analytics #python #data science course #datascience #lejhro #data privacy #big data #machinelearning

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innonurse · 7 months ago

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NIH research highlights the risks and benefits of incorporating AI into medical decision-making

- By InnoNurse Staff -

Researchers at the National Institutes of Health (NIH) found that an AI model accurately answered medical quiz questions designed to test health professionals' diagnostic abilities using clinical images and text summaries. However, the AI made mistakes in describing images and explaining its decision-making.

The study, published in npj Digital Medicine, highlights AI's potential in clinical settings but emphasizes that human experience remains crucial for accurate diagnosis.

The research involved comparing the AI model's and physicians' answers to questions from the New England Journal of Medicine's Image Challenge. The AI often selected the correct diagnosis more frequently in closed-book settings, but physicians outperformed the AI when using external resources, particularly on more difficult questions.

The study suggests further evaluation of multi-modal AI technology is needed before clinical integration, noting its potential to augment clinical decision-making with data-driven insights.

The study used GPT-4V and was a collaborative effort across several institutions.

Read more at NIH

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Other recent news and insights

A new video-based test for Parkinson's employs AI to monitor the disease's progression (University of Florida)

India: Acko acquires health tech startup OneCare (The Economic Times)

#ai #medtech #health tech #digital health #india #parkinsons #neuroscience #aging #acko #onecare

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ryjuegos · 7 months ago

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The Future of Artificial Intelligence in Technology: Trends and Predictions for 2024

Welcome to an exciting look at the future of artificial intelligence (AI) in technology. As we head towards 2024, AI is changing fast. It's bringing new ways to live, work, and interact with the world. The past year saw a big jump in generative AI, catching everyone's attention and starting to blend into business.Now, 2024 looks like a key year. Researchers and companies are working hard to make this new tech a part of our everyday lives. The growth of generative AI shows how computers are changing, moving from big, complex models to smaller, smarter ones.Open-source projects are leading the way in innovation. They're making AI more accessible to everyone, not just big companies. In this article, you'll learn about the main trends and predictions for AI's future. You'll see how AI will become more open, how it will work with different kinds of data, and how it will make experiences more personal.Key Takeaways - Generative AI has seen significant growth in public awareness and business integration since 2022. - Open-source large language models are challenging proprietary solutions, driving innovation and democratization. - Smaller, more efficient AI models are becoming popular, enabling edge computing and improved explainability. - Multimodal AI models that can process text, images, and video are emerging, unlocking new applications. - AI regulation and governance are gaining traction to ensure responsible and ethical deployment.

Democratization of AI: Smaller Models and Open-Source Advancements

The democratization of artificial intelligence (AI) is changing the tech world. Smaller AI models are getting more powerful and easy to use. This makes AI available to more people. Open-source projects are also pushing innovation forward, making AI more accessible to everyone.Empowering AI Adoption with Smaller ModelsGartner says democratized AI will be a big change in the next decade. It's all about making AI tools and skills available to everyone. Smaller AI models like Google Cloud AutoML and IBM's Watson are making AI useful for all kinds of businesses. This makes AI easier for people who aren't experts to use. - AI tools like Tableau and Power BI make it easy to understand complex data. They help users get insights fast without needing special skills. - Tools such as Miro and Mural use AI to help with brainstorming and showing ideas. This shows how AI can help teams work together better. - AI tools like Copy.ai and Jasper help with making content by creating text from prompts. This makes making content faster. AI is making things more efficient, customer-focused, and profitable in many industries. It's letting more people use this powerful technology.Open-Source Initiatives Driving InnovationOpen-source projects are also key in making AI better and more accessible. Projects like TensorFlow, PyTorch, and Scikit-learn let people work together on AI. This makes AI development more open and inclusive."The democratization of AI is speeding up progress, sparking new ideas, and opening doors for big changes in society."Platforms like GitHub help developers work together on AI projects. These efforts are narrowing the gap with commercial AI, making advanced AI tools available to more people.

The trend of democratizing AI is giving more people the chance to use this technology. With smaller AI models and more open-source projects, the future of AI looks bright. It will bring more innovation and make the tech world more open to everyone.

Multimodal AI: The Next Frontier

The future of artificial intelligence (AI) is set to make a huge leap with multimodal AI models. These advanced systems can handle and analyze different types of data like text, images, and video. This opens up new possibilities for more natural and flexible uses.Models like OpenAI's GPT-4V and Google's Gemini are at the forefront of this change. They can switch easily between tasks that involve understanding language and tasks that involve seeing images. This lets them understand the world in a deeper way.Unlocking Diverse InsightsMultimodal AI models work best when they use different kinds of data together. This helps them find insights that would be hard or impossible to get from just one type of data. For example, in healthcare, these models can look at medical images, patient stories, and genetic info to spot diseases early and treat them better. Autonomous cars also use this kind of AI to make sense of camera, radar, and sensor data for safer driving.But the advantages of multimodal AI aren't just for these specific areas. In customer service, advanced chatbots can understand text, voice, and facial expressions for better conversations. In entertainment, it makes games and movie recommendations more engaging by combining what we see and hear.As multimodal AI keeps getting better, the future looks very exciting. By using a mix of data types, these models will open up new ways to innovate. They will change industries and how we use technology in our daily lives."Multimodal AI represents the next frontier in artificial intelligence, enabling us to unlock unprecedented levels of understanding and create applications that are more intuitive, versatile, and impactful than ever before."

Personalized AI: Tailored Experiences and Insights

Personalized AI and tailored AI-powered experiences are changing how we use technology. Companies use AI-powered experiences to make interactions more personal and engaging. They focus on what each user likes and needs.Big names like Google, Apple, and Samsung lead this change. They focus on AI personal assistants that work well with users' data. This makes customer service more insightful and personal than ever. - Fast-growing companies gain 40% more revenue from personalization than slower-growing companies. - 71% of consumers expect personalized experiences from companies. - 76% of consumers get frustrated when they don't get the personal touch they expect. AI lets businesses analyze lots of data and predict what customers might want next. This means they can send targeted marketing messages. Personalized AI grabs customers' attention and builds stronger bonds with them."AI personalization helps businesses present the right products or services, increasing the likelihood of conversion and revenue growth."But, using AI-powered experiences comes with challenges. Companies must keep things human and follow strict data rules. Finding the right mix of tech and personal touch is key. This way, businesses can make the most of personalized AI and give customers experiences they love.

Artificial Intelligence in Technology: Trends and Predictions

2024 is set to bring big changes and new trends in artificial intelligence (AI). The big language models will keep leading the way. But, it's important for businesses and tech fans to watch for new AI advancements.Big tech companies are facing a challenge to make money from their generative AI. This might lead to easier-to-use platforms for everyone. These platforms will let more people use powerful language models.Text-to-video is another big area in AI. Startups like Runway are making high-quality videos easily. But, they need to fix issues with reliability and bias in these AI models.AI isn't just about language and making content. It's changing how industries work and making things more efficient. AI helps businesses make better decisions and improve customer experiences with tools like personalized marketing and predictive analytics.As AI gets more popular, companies need to use it wisely and think about ethical AI. The future of AI is bright, and those who focus on responsible AI will lead the way."AI is no longer a futuristic concept; it is transforming how we live, work, and interact with technology. The trends and predictions for 2024 showcase the incredible potential of this technology to revolutionize various industries and create new opportunities for businesses and consumers alike."

Governance and Responsible AI

AI technology is becoming more common, making us focus on governance, safety, and security. Companies now see the importance of keeping AI safe and secure. This is to protect their good name. Big companies are setting clear rules for their AI use. They aim to be innovative yet responsible, avoiding mistakes and ensuring fairness.Data governance tools are key for responsible AI. They help organize and automate how data is handled. This keeps companies in line with laws and makes sure their AI is trustworthy.The Importance of AI GovernanceAI governance is a team effort in big companies. The CEO and top leaders set the rules for using AI responsibly. They see AI as a way to improve customer service, change business for the better, and make things more efficient. But, there are worries about AI being used ethically and fairly."Responsible AI is about designing, building, and deploying AI systems in a manner that empowers individuals and businesses while ensuring equitable impacts on customers and society." - World Economic ForumIt's important to have clear rules for AI use. This means making sure AI goals match business goals, checking risks, and making sure data and models are used right. Following laws and guidelines is also key.Keeping an eye on AI systems is crucial for their performance and fairness. As AI grows in fields like healthcare and finance, having good AI rules is more important than ever.

Domain-Specific AI Models in the Enterprise

Enterprises are moving towards specialized AI models. These models are more accurate and efficient, offering real value in business. This marks a new chapter in AI for companies.Companies are seeing the perks of AI models made for their specific needs. For instance, a healthcare firm uses a GPT model for precise cost predictions. A software company uses MagnifAI to improve customer analysis and upselling.Domain-specific AI models bring many benefits. They give better results, speed up progress, and solve problems cost-effectively. These models use pre-trained data from various industries to meet specific sector or use case needs.The global market for generative AI is set to hit $110.8 billion by 2030. Companies are fine-tuning these models for their unique data to beat general AI's limits. This is changing how e-commerce, product design, and quality control work."Domain-specific AI solutions like MagnifAI provide more efficiency and help generate better business decision-making insights."The future of AI in business is about using specialized models for unique challenges. By adopting domain-specific AI, companies can solve big problems faster. This leads to more ROI and a competitive edge online.

AI Regulation and Compliance

As AI technology gets better, governments and companies are working on rules for it. They worry about risks like threats to security and rogue AI. Some companies see rules as a way to grow, while others aim for responsible AI use.The European Union has made big steps with its AI Act. This could set a standard for AI rules worldwide. In the US, some states have made their own AI laws, and more might do the same. The American Data Protection and Privacy Act and a White House Executive Order also address AI rules.Companies focus on AI compliance to protect their reputation. Legal advice is key for checking if AI rules are followed. Companies using AI must make sure it's fair, transparent, and doesn't change in bad ways."Unregulated AI may lead to unfair outcomes due to the amplification of biases in data, with evolving algorithms that are difficult to explain and may change based on new data."There's no single US law for AI yet, but top AI governance companies are making their own rules. As people learn more about AI risks, the need for strong laws grows.The rules for AI are changing fast. Companies and users must keep up with AI compliance to use this powerful tech right.

The Rise of Personal AI Assistants

AI is getting more personal and tailored for each user. This means personal AI assistants will become a big deal soon. Google Gemini and Google Workspace use your data to give you personalized experiences and insights.Apple and Samsung are focusing on on-device AI for privacy and quick access. This means your personal AI could be a big part of your life. It could help with relationships, learning, and your career by giving you advice and recommendations.AI-powered virtual assistants are getting more common at home. Siri and Alexa show how well they can understand and respond to what you say. These assistants make using technology better, more engaging, and keep customers coming back.As technology gets better, expect to see AI assistants that are smarter, more flexible, and fit your needs perfectly. They will make managing your digital life easier and more efficient. Read the full article

#AITrends2024 #FutureTechInnovations #TechnologyPredictions

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jhavelikes · 10 months ago

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We are excited to introduce our largest and most capable model yet, Reka Core. It is a frontier-class multimodal language model on par with leading models in the industry today. Core was efficiently trained from scratch on thousands of GPUs over a period of a few months. Performance highlights Core is competitive with models from OpenAI, Anthropic, and Google across key industry-accepted evaluation metrics. Given its footprint and performance, on a total cost of ownership basis, Core delivers outsized value. The combination of Core’s capabilities and its deployment flexibility unlocks vast new use cases. Core is comparable to GPT-4V on MMMU, outperforms Claude-3 Opus on our multimodal human evaluation conducted by an independent third party, and surpasses Gemini Ultra on video tasks. On language tasks, Core is competitive with other frontier models on well-established benchmarks.

Reka Core: Our Frontier Class Multimodal Language Model — Reka AI

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wbtodays · 1 year ago

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Bajaj Pulsar 500 Twinner Cafe Racer: Exploring The New Pulsar

The Pulsar 500 Twinner Cafe Racer a Trademarked Bike 2024

Bajaj Auto, a big name in India's bike world, has something exciting up its Pulsar 500 Twinner Cafe Racer. Get set for an awesome ride with the Pulsar 500 Twinner Cafe Racer! It's not a myth anymore – it's a real blend of classic cool and modern muscle. Inside, a powerful 500cc twin-cylinder engine is ready to unleash 60 horses of pure excitement. With a sleek design and no extra frills, it effortlessly cruises through the air. Low handlebars and a solo seat make you one with the bike, while the teardrop fuel tank and clipped tail keep the focus on speed.

This bike takes you back to the carefree days of wide roads, cozy leather coats, and wind in your hair. It's more than just a simple bicycle. Seizing the opportunity to embark on an incredible journey is imperative, as the Pulsar 500 Twinner Cafe Racer is not just following but also leading the road.

Bajaj Pulsar 500 Twinner Cafe Racer (Expected Specifications)

The Pulsar 500 Twinner Cafe Racer is not yet officially launched, and these are estimated specifications based on rumors and reports. FeatureSpecificationEngineTwin-cylinder, 4-valve, DOHC, liquid-cooledDisplacement499cc (estimated)Power60 bhp (estimated)Torque50 Nm (estimated)Transmission6-speed gearboxChassisPerimeter frameSuspensionTelescopic forks (front), monoshock (rear)BrakesDisc brakes (front and rear)ABSDual-channel ABS (expected)TiresTubeless tires (likely)Fuel Tank Capacity15 liters (estimated)Seat Height780mm (estimated)Weight180kg (estimated)

Double the Power Fun

For a brand known for single-cylinder bikes, Bajaj is cooking up a surprise—a twin-cylinder engine. Imagine the thrill! Could it be the answer to the cool success of Royal Enfield's twin-cylinder 650 platform? Rumor has it that Bajaj and KTM were planning a 490cc twin-cylinder engine in 2020, and they even trademarked it as Twinner.

Twinner: No Frills, Just Thrills

Bajaj's Twinner trademark is refreshingly simple. No extra tags. This hints at it blending smoothly with existing models like the Pulsar. Picture this: Bajaj Pulsar 500 Twinner, where Twinner becomes the stamp of a new riding experience.

Style and Substance by Design

A guy named Pratyush Rout is behind the cool design. He mixed Pulsar's tough look with classic touches. Think round headlights like the first Pulsar, a sporty stepped rear seat, and a tweaked fuel tank. It's like a mashup of styles from different bike worlds.

Features That Turn Heads

The Pulsar 500 Twinner borrows some cool stuff from the Dominar 400, like those round headlights that nod to motorcycle history. The instrument cluster rocks twin circular pods, adding a touch of class. Golden front forks and Nissin calipers on disc brakes make it look even more awesome. If it hits the market, it'll outshine the soon-to-be-launched 400cc single-cylinder Pulsar.

Performance in the Spotlight

Get ready for some power! The Pulsar 500 Twinner aims for around 60 bhp of power and 50Nm of torque, thanks to a wider engine. It's not just about power; it's packed with modern features like a 4V head, DOHC setup, liquid cooling, quick-shifter, throttle-by-wire, and a slipper clutch for a super fun ride. https://www.youtube.com/watch?v=ZE7yAKPcuiw&pp=ygUOVGhlIFB1bHNhciA1MDA

Wrapping it Up

Bajaj's dive into twin-cylinder engines, hinted by the Twinner trademark and the sneak peek of the Pulsar 500 Twinner, is a big move. It's a mix of cool design and powerful performance, setting the stage for an exciting chapter in Bajaj's motorcycle story.

FAQs

When can I get my hands on the Pulsar 500 Twinner?The launch date is still a secret. Keep an eye on Bajaj Auto's updates for the scoop.How does the Pulsar 500 Twinner compare to other bikes?We'll spill the beans after experts review the bike post-launch.What's the deal with the Pulsar 500 Twinner's engine?Expect a power-packed experience with modern features for an upgraded ride.Is the Pulsar 500 Twinner priced just right?No word on pricing yet, but Bajaj Auto usually keeps it competitive.Can I jazz up my Pulsar 500 Twinner?Customization details will roll out post-launch. Stay tuned for ways to personalize your ride! Read the full article

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tvsnotor · 11 months ago

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govindhtech · 1 year ago

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Latest Marvels : Azure AI Data & Digital Apps Advancements

Azure AI Data, AI, and Digital Apps updates:

Modernize data, build smart apps, and use AI

Generational AI models and tools have improved application and business process experiences for years, but this year was a turning point. Within months, customers and partners integrated AI into their transformation roadmaps and launched AI-powered Digital Apps and services.

A new technology has never caused such rapid change. It shows how many organizations were AI-ready and how cloud, data, DevOps, and transformation cultures prepared them. Customers and partners can maximize AI with hundreds of Microsoft resources, models, services, and tools this year.

New models and multimodal capabilities in Azure AI

They offer the most advanced open and frontier models so developers can build confidently and unlock immediate value across their organization.

They added Models as a Service to Azure OpenAI Service last month. Azure AI applications can use model providers’ latest open and frontier LLMs.

MaaS for Llama 2 was announced last week. The ready-to-use API and token-based billing of MaaS for Llama 2 let developers integrate with their favorite LLM tools like Prompt Flow, Semantic Kernel, and LangChain. Hosted fine-tuning lets generative AI developers use Llama 2 without GPUs, simplifying model setup and deployment. Llama 2, purchased and hosted on Azure Marketplace, lets them sell custom apps. In his blog post, John Montgomery describes this announcement and shows Azure AI Model Catalog models.

Here are improving Azure OpenAI Service and launched multimodal AI to let businesses build

Generative AI experiences with image, text, and video:

Preview DALL·E 3: Generate images from text descriptions. The AI model DALL·E 3 excels in this regard. DALL·E 3 generates images from user descriptions.

General availability: GPT-3.5 Turbo preview at

16k token prompt:

GPT-4 Turbo: Azure OpenAI Service models now extend prompt length and improve generative AI application control and efficiency.

Visionary GPT-4 Turbo preview: GPT-4V optimises experiences by generating text output from images or videos using Azure AI Vision enhancements like video analysis.

Modifying Azure OpenAI Service models: Fine-tune Azure OpenAI Service models Babbage-002, Davinci-002, and GPT-35-Turbo. Developers and data scientists can customize Azure OpenAI Service models. Discover fine-tuning.

GPT-4 updates: Azure OpenAI Service can fine-tune GPT-4. Organizations can customize the AI model by fine-tuning. It’s like customizing an AI suit. Checking GPT-4 updates.

Frontier steering: Prompting power grows

GPT-4 prompting dazzles! Microsoft Research recently blogged about promptbase, a reasoning-based GPT-4 prompt. Other AI models lag behind GPT-4 in various test sets, including those used to benchmark the recently announced Gemini Ultra. Zero-shot chain-of-thought prompting. See the blog post and try these GitHub resources.

LLMOps RAI tools and practices

Safety boundaries supporting short- and long-term ROI must be considered as AI adoption matures and companies produce AI apps. This month’s LLMOps for business leaders article covered integrating responsible AI into your AI development lifecycle. These Azure AI Studio best practices and tools help development teams apply their principles.

AI Advantage from Azure

Cloud database Azure Cosmos DB uses AI. Built-in AI, natural language queries, vector search, and simple Azure AI Search integration are supported. To demonstrate these benefits, her new Azure AI Advantage offer gives new and existing Azure AI and GitHub Copilot customers 40,000 RU/s of Azure Cosmos DB for 90 days.

Modern data and analytics platforms are essential for AI transformation because intelligent apps need data.

Multinational law firm Clifford Chance benefits clients with new technology. The company built a solid Azure data platform to test Azure AI, Microsoft 365 Copilot, and large language models. Cognitive translation is an IT team’s fastest-growing product.

Azure Machine Learning and Azure Databricks helped Belgian insurance company Belfius reduce development time, efficiency, and reliability. Data scientists can create and transform features while the company improves fraud and money laundering detection.

Co-innovation Azure-Databricks improves AI experiences

Customers and partners shared how maturing AI tools and services are helping them achieve more at Microsoft Ignite 2023 in November.

One of her strategic partners, Databricks offers Azure’s fastest-growing data services. Azure Databricks’ interoperability with Microsoft Fabric and use of Azure OpenAI to improve customer AI experiences were recently demonstrated. Customers can build retrieval-augmented generation (RAG) applications on Azure Databricks and analyze the output with Power BI in Fabric using Azure OpenAI LLMs.

Azure Database PostgreSQL AI extension

With the new Azure AI extension, Azure OpenAI LLMs can generate vector embeddings and build rich PostgreSQL generative AI applications. These powerful new capabilities and pgvector support make Azure Database for PostgreSQL another great place to build AI-powered apps.

SQL Server anywhere gets Azure Arc cloud innovation

SQL Server manageability and security improvements from Azure Arc are available this month. Customers can optimize database performance and gain critical SQL Server estate insights with SQL Server monitoring. Azure portal makes Always On availability groups, failover cluster instances, and backups more visible and simple.

Lower Azure SQL Database prices Calculate hyperscale

The new Azure SQL Database price Hyperscale gives cloud-native workloads Azure SQL performance and security at commercial open-source database prices. For scalable, AI-ready cloud applications of any size and I/O, hyperscale customers can save 35% on compute resources.

Apps change operations and experiences

Personalized employee apps and customer chatbots are examples of digital applications developed and deployed by companies using AI to improve operations and experiences. Updates like these enable innovation.

Custom copilots and the seven AI development pillars

Copilots are exciting, and Azure AI Studio in public preview lets developers build generative AI apps. Businesses must carefully design a durable, adaptable, and effective approach for this new era. What can AI developers do for customer engagement? Consider these seven pillars for your custom copilot.

AKS is a top cloud-native intelligent app platform

AI and Kubernetes will influence app development. AI and cloud-native collaboration drives innovation at scale, with Azure Kubernetes Service (AKS) supporting compute-intensive workloads like AI and machine learning. Brendan Burn’s KubeCon blog describes how Microsoft builds and supports customer-beneficial open-source communities.

Azure enables unlimited innovation.

Her recent portfolio news, resources, and features, especially digital applications, have received great tech community and customer response.

Ignite’s Platform Engineering Guide is a hit, demonstrating demand for this training.

Technology innovation in companies is crucial.

Two recent customer stories caught my eye.

Modernizing LEGO House interactive experiences with Azure Kubernetes

Here helping The LEGO House in Denmark, the ultimate LEGO experience center for kids and adults, migrate custom-built interactive digital experiences from an aging on-prem data center to Microsoft Azure Kubernetes Service (AKS) to improve stability, security, and iteration and collaboration on new guest experiences. LEGO House updates experiences faster with this cloud move and guest feedback. The modernizing destination hopes to share knowledge and technology with LEGOLAND and brand retail stores.

Gluwa chose Azure for a reliable, scalable cloud solution to bring banking to emerging, underserved markets and close the financial gap.

An estimated 1.4 billion people struggle to get credit or personal and business loans in a country with limited financial infrastructure. Borderless financial technology from blockchain helps Gluwa with Creditcoin stand out. The Azure cloud supports it. Gluwa has a solid platform with her.NET framework, Azure Container Instances, AKS, Azure SQL, Azure Cosmos DB, and more. The business is more efficient due to reliable uptime, stable services, and rich product offerings.

CARIAD builds Volkswagen Group vehicle service platform with Azure and AKS

The Volkswagen Group’s software subsidiary CARIAD created the CARIAD Service Platform with Microsoft using Azure and AKS to provide automotive applications to Audi, Porsche, Volkswagen, Seat, and Skoda as the industry moved to software-defined vehicles. This platform lets CARIAD developers develop and service vehicle software, giving Volkswagen an edge in next-generation automotive mobility.

AKS and Azure Arc help DICK’S Sporting Goods provide omnichannel service

To create a more consistent, personalized customer experience across its 850 stores and online retail experience, DICK’S Sporting Goods envisioned a “one store” technology strategy to write, deploy, manage, and monitor its store software across all locations nationwide and reflect those experiences on its eCommerce DICK’S needed modularity, integration, and simplicity to integrate its public cloud and edge computing systems.

DICK’s Sporting Goods is using Azure Arc and Azure Kubernetes Service to migrate its on-premise infrastructure to Azure and create an adaptive cloud environment. The retailer can now easily deploy new apps to every store for ubiquity.

Performance and efficiency of Azure Cobalt for intelligent apps

Azure has hundreds of cloud-native and intelligent application performance services. Azure silicon performance and efficiency efforts have expanded. Azure Maia, her first custom AI accelerator series for cloud-based AI training and inference, and Azure Cobalt, her first Microsoft Cloud CPU, were launched recently.

Azure Arm chips perform 40% slower than Cobalt 100, the first 64-bit 128-core chip in the series, which runs Microsoft Teams and Azure SQL.