Google's PaLM 2: Revolutionizing Language Modeling with Multilingual Proficiency, Reasoning Abilities, and Coding Proficiency. ¹​

Discover the cutting-edge advancements in AI from Google as they unveil PaLM 2, their next-generation language model. PaLM 2 has undergone extensive training on multilingual text, enabling it to understand, generate, and translate nuanced language across more than 100 languages. With improved reasoning capabilities and proficiency in programming languages, PaLM 2 demonstrates its potential for logic, common sense reasoning, mathematics, and coding tasks.

Google has already integrated PaLM 2 into over 25 products and features, empowering users worldwide with enhanced language generation, efficient workspace features, and productivity tools. Through the development of PaLM 2, Google continues to drive innovation in AI and deliver real-world benefits in areas like healthcare and creative endeavors.

Meta's AI breakthrough: Speech recognition for 1,000+ languages now open source, paving the way for language preservation and universal communication. ²​

Meta's AI breakthrough empowers speech recognition in over 1,000 languages, a significant leap towards preserving endangered languages. The company is sharing these models as open source on GitHub, enabling developers to build inclusive speech applications for diverse languages. Existing speech recognition models cover a mere fraction of the world's 7,000 languages due to limited labeled training data. Meta overcame this challenge by retraining their AI model to learn speech patterns from audio, requiring minimal data. Their models can converse in over 1,000 languages and recognize more than 4,000, with half the error rate compared to rival models. While there are risks of mistranscription and biased words, Meta's advancements have far-reaching implications for language preservation and global communication.

AI Revolutionizes Antibiotic Discovery: Unveiling a Breakthrough Against Hospital Superbugs. ³​

Scientists from McMaster University have utilised artificial intelligence (AI) to uncover a breakthrough antibiotic called abaucin, which shows promising potential in combating drug-resistant infections, particularly Acinetobacter baumannii. This bacterium poses a significant threat in hospitals and is known to cause severe conditions like pneumonia and meningitis.

The traditional methods of antibiotic discovery have proven challenging and time-consuming. However, AI algorithms allowed researchers to swiftly assess millions of molecules, leading to the identification of abaucin. Unlike broad-spectrum antibiotics, abaucin specifically targets A. baumannii, reducing the risk of drug resistance development and opening doors to more precise and effective treatments. This study underscores the immense potential of AI in revolutionising antibiotic discovery and providing hope in the fight against deadly hospital superbugs.

Unleash Your Creativity: Photoshop's AI Transforms Your Images with a Single Text Prompt. ⁴​

Adobe has introduced a groundbreaking AI tool called Generative Fill in its Photoshop application, leveraging generative AI to add or remove objects from photos based on a simple text prompt. Acting as an "AI co-pilot," Adobe Firefly powers this feature, aiming to revolutionize the photo editing process. While enhancing user creativity, Adobe acknowledges the need to address concerns about potential misuse of the technology. The addition of Generative Fill is expected to usher in a new era of AI-driven creativity in the creative industries, providing extraordinary results and streamlining previously time-consuming tasks. Currently available in the beta version, a wider release of this transformative AI tool in Photoshop is on the horizon.

Safeguarding the Digital Frontier: Exploring the Reality of AI in Cybersecurity. ⁵​

In the realm of cybersecurity, the long-awaited promise of artificial intelligence (AI) is becoming a reality. AI-driven capabilities have evolved from simple rule-based systems to sophisticated tools that leverage generative AI and contextualise vast amounts of data. This breakthrough empowers cybersecurity teams with game-changing speed and accuracy, providing them with a much-needed advantage in their ongoing battle against cybercriminals. With a skills shortage and data explosion posing challenges, matured AI capabilities address these obstacles by automating tasks, improving defence postures, and enabling precise actions.

By combining AI with automation, security teams can achieve reliable speed and enhance their ability to detect, investigate, and respond to threats. The integration of AI into threat detection and response technologies, such as IBM's QRadar Suite, amplifies the effectiveness of security operations centres (SOCs) and streamlines the incident lifecycle. With AI's assistance, SOC teams can prioritise real threats amidst the noise, accelerate investigation and response processes, and significantly enhance overall resilience and readiness in the cybersecurity industry.

Spotify's Potential AI Breakthrough: AI-Generated Podcast Ads. ⁶​

In a recent podcast episode, Bill Simmons shared that Spotify is reportedly working on AI technology that would allow podcast hosts to create host-read ads without having to personally record them. This development could offer podcasters exciting opportunities, including the creation of geo-targeted and multilingual ads, while saving valuable time for content creation. Although Spotify has not officially confirmed these claims, their ongoing investment in AI technology, exemplified by the introduction of AI DJ, suggests the possibility.

The advent of AI-generated podcast ads would revolutionise the industry, offering podcasters a time-saving alternative and the potential to reach a broader audience. However, concerns regarding authenticity and the risk of misinformation should also be considered. The development of AI-generated podcast ads marks a significant milestone in podcasting, with the future implementation and audience response eagerly anticipated.

Chegg vs. ChatGPT: The Battle for AI-Powered Education Dominance. ⁷​

Chegg, an online education company, and its encounter with the disruptive force of generative AI, particularly OpenAI's ChatGPT. Chegg's executives had previously considered the potential of AI to replace human instructors and reduce costs but underestimated the rapid pace at which consumers embraced tools like ChatGPT.

Initially, Chegg didn't view ChatGPT as a threat to its paid services. However, when OpenAI released GPT-4, students began opting for ChatGPT instead of Chegg's paid offerings, leading to a significant loss in subscriber growth and a decline in the company's market value.

Despite their efforts, Chegg's future remains uncertain, and the company's executives are focused on navigating the challenges posed by generative AI to stay relevant in the education industry.

Unlock the mystery of AI with this epic multi-part series, taking you on a journey from its humble beginnings to the present day. Join us as we explore different aspects of Artificial Intelligence in this entire series.

Artificial Intelligence (AI) is an exciting and rapidly growing field that has the potential to transform our world in countless ways. From self-driving cars to virtual personal assistants, AI has already made its way into many aspects of our daily lives, and its applications continue to expand.

Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines “man-made,” and intelligence defines “thinking power”, hence AI means “a man-made thinking power.”

Artificial Intelligence is a branch of computer science by which we can create intelligent machines which can behave like a human, think like humans, and able to make decisions.

But what exactly is AI, and how did it come to be? In this article, we’ll take a brief look at the history of AI and its evolution over time.

Early Concepts of AI​

The idea of machines that could mimic human intelligence dates back centuries, with early examples including the ancient Greek myths of Talos, a giant bronze statue that could move and act on its own, and Pygmalion’s statue, which was brought to life by the goddess Aphrodite.

Figure 1 — Talos by Adrienne Mayor

In the modern era, the concept of AI began to take shape in the mid-20th century. In 1950, computer scientist Alan Turing proposed the “Turing Test,” which is still used today to measure a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.

Early AI research focused on rule-based systems, in which machines were programmed to follow a set of explicit rules to solve problems. While these systems were effective for some tasks, they were limited by their inflexibility and inability to learn from experience.

AI has many sub-categories like Machine Learning and Deep Learning. People usually tend to use these terms interchangeably. Let’s look at these terms in brief -

Figure 2 — By the Author — Types of Artificial Intelligence

The Rise of Machine Learning​

In the 1980s, a new approach to AI emerged: Machine Learning. This approach involved designing algorithms that could learn from data, identify patterns, and make predictions based on that data.

Machine learning algorithms were initially used for simple tasks such as recognizing handwritten characters, but their applications quickly expanded to more complex problems, including speech recognition, image recognition, and natural language processing.

Deep Learning and Neural Networks​

In the 2010s, a subfield of machine learning known as deep learning began to emerge, fueled by advances in computing power and data storage. Deep learning algorithms are modeled after the structure and function of the human brain, using artificial neural networks to simulate the behavior of neurons and synapses.

Deep learning has enabled breakthroughs in areas such as image and speech recognition and has led to the development of autonomous vehicles and other advanced technologies.

The Future of AI​

As AI continues to advance, its potential applications are virtually limitless. From personalized healthcare to smart homes and cities, AI has the power to revolutionize nearly every aspect of our lives.

As Uncle Ben says, “With great power comes great responsibility.” AI systems become more sophisticated, it’s important to ensure that they are designed and deployed ethically and responsibly, with consideration for issues such as bias, privacy, and security.

Types of Artificial Intelligence​

Figure 3 — By the Author — Types of Artificial Intelligence

Artificial Intelligence — Type 1: Based on Capabilities​

Narrow AI

• Narrow AI is a type of AI that is able to perform a dedicated task with intelligence.
• Narrow AI cannot perform beyond its field or limitations, as it is only trained for one specific task. Hence it is also termed weak AI. Narrow AI can fail in unpredictable ways if it goes beyond its limits.
• Some Examples of Narrow AI are playing chess, purchasing suggestions on e-commerce sites, self-driving cars, speech recognition, and image recognition.

General AI

• General AI is a type of intelligence that could perform any intellectual task with efficiency like a human.
• The idea behind the general AI is to make such a system that could be smarter and think like a human on its own.
• It is currently a hypothetical concept, and we don’t yet have any real-world examples of this type of AI.

Super AI

• Super AI is a level of Intelligence of Systems at which machines could surpass human intelligence, and can perform any task better than humans with cognitive properties. It is an outcome of general AI.
• Super AI is still a hypothetical concept of Artificial Intelligence. The development of such systems in real is still world changing task.

Artificial Intelligence — Type 2: Based on Functionality​

Reactive Machines

• Purely reactive machines are the most basic types of Artificial Intelligence.
• Such AI systems do not store memories or past experiences for future actions.
• These machines only focus on current scenarios and react to them as per the possible best action.
• Examples: IBM’s Deep Blue system, Google’s AlphaGo

Limited Memory

• Limited memory machines can store past experiences or some data for a short period of time.
• These machines can use stored data for a limited time period only.
• Self-driving cars are one of the best examples of Limited Memory systems. These cars can store the recent speed of nearby cars, the distance of other cars, the speed limit, and other information to navigate the road.

Theory Of Mind

• Theory of Mind AI should understand human emotions, people, and beliefs, and be able to interact socially like humans.
• This type of AI machine is still not developed, but researchers are making lots of efforts and improvements for developing such AI machines.

Self-Awareness

• Self-awareness AI is the future of Artificial Intelligence. These machines will be super intelligent and will have their own consciousness, sentiments, and self-awareness.
• These machines will be smarter than the human mind.
• Self-Awareness AI does not exist in reality still and it is a hypothetical concept.

Conclusion​

Artificial intelligence has come a long way since the early days of rule-based systems, and its evolution shows no signs of slowing down. With ongoing research and development, the potential applications of AI are vast and ever-expanding.

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Unlock the Power of BingAI: Experience the Future, Today! ¹​

Microsoft has made Bing AI accessible to the public, eliminating the waitlist requirement. Users can now try out the AI bot by signing in to Bing using their Microsoft account on the Edge browser. The latest update introduces several exciting features powered by OpenAI's technologies.

Also, Bing AI now supports rich "visual answers," displaying graphs, charts, and formatted content. The Bing Image Creator has been upgraded to support over 100 languages, enabling the generation of AI images based on text prompts and visual examples. Additionally, users can export and share chats, benefit from improved summarization capabilities for long documents, and enjoy actions within Edge for quicker access to relevant content. Microsoft is also developing third-party plug-ins to expand functionality within Bing Chat.

LinkedIn's Intelligent Assistance: Craft the Perfect Job Application ²​

LinkedIn is reportedly testing an AI-powered feature that provides personalized writing suggestions for job seekers, aiming to help them create tailored job applications. The feature generates short cover letter-like messages using information from the user's profile, the hiring manager's profile, the job description, and the targeted company. While the AI-generated drafts serve as a starting point, LinkedIn emphasizes the importance of customization and encourages users to review and edit the suggestions to reflect their own voice and style.

This development builds upon LinkedIn's existing AI writing tool for profile creation. The adoption of AI in job application drafting reflects the growing interest in artificial intelligence, with its potential to enhance user experiences and improve outcomes in various industries, including recruitment and career development.

YOLO-NAS: Revolutionizing Object Detection with Unprecedented Precision ³​

Deci AI, a deep learning firm, has unveiled YOLO-NAS, its latest deep learning model designed for real-time object detection with remarkable performance. Built on Deci's Neural Architecture Search Technology, AutoNAC™, YOLO-NAS outperforms other models like YOLOv6, YOLOv7, and YOLOv8, including the recently launched YOLOv8. AutoNAC democratizes Neural Architecture Search, enabling organizations to create customized, fast, accurate, and efficient deep learning models quickly.

YOLO-NAS delivers superior throughput, achieving 50% more throughput and 1 mAP higher accuracy compared to other YOLO models. It is pre-trained on popular datasets, making it suitable for various real-world applications. The open-source model is available with pre-trained weights for non-commercial research use on Deci's PyTorch-based computer vision training library called SuperGradients.

Introducing StarCoder: Free Code-Generating Assistant ⁴​

Hugging Face and ServiceNow Research have jointly released StarCoder, a free code-generating model that offers an alternative to existing AI systems like GitHub's Copilot. StarCoder, part of the BigCode project, was trained on over 80 programming languages and integrates with Microsoft's Visual Studio Code editor. Unlike other commercial models, StarCoder is royalty-free and available for use by anyone, including corporations.

The project aims to develop state-of-the-art AI systems for code generation in an open and responsible manner. StarCoder's release comes amidst debates around the use of public source code and licensing agreements for training AI models, with efforts made to address privacy concerns and adhere to ethical best practices.

Geoffrey Hinton: AI's Threat Could Be 'More Urgent' Than Climate Change ⁵​

In a recent interview, renowned AI pioneer Geoffrey Hinton expressed his concerns that the threat posed by artificial intelligence (AI) to humanity could be even more urgent than climate change. Hinton, often referred to as one of the "godfathers of AI," believes that the risks associated with AI technology are significant and warrant immediate attention. Having recently left Alphabet, Hinton intends to speak out about these risks without any constraints from his former employer. As the debate around AI's impact on society continues to unfold, Hinton's remarks highlight the need for careful consideration and proactive measures to ensure the responsible and ethical development and deployment of AI technologies.

Accelerating the Quest for New Metals: ML Offers a Promising Solution ⁶​

Machine learning could help develop new types of metals with useful properties, such as resistance to extreme temperatures and rust, according to new research. This could be useful in a range of sectors—for example, metals that perform well at lower temperatures could improve spacecraft, while metals that resist corrosion could be used for boats and submarines. Usually they start off with one well-known element, like iron, which is cheap and malleable, and add one or two others to see the effect on the original material. It’s a laborious process of trial and error that inevitably yields more duds than useful results. Researchers from the Max Planck Institute managed to identify 17 promising new metals using this method.

Revolutionizing ML: Researchers Unveil a More Agile Approach ⁷​

Artificial intelligence researchers have celebrated a string of successes with neural networks, computer programs that roughly mimic how our brains are organized. In 2020, two researchers at the MIT led a team that introduced a new kind of neural network based on real-life intelligence — but not our own. After a breakthrough last year, the novel networks may now be versatile enough to supplant their traditional counterparts for certain applications.

Liquid neural networks offer an elegant and compact alternative , said Ken Goldberg, a roboticist at the University of California, Berkeley. These networks can run faster and more accurately than other so-called continuous-time neural networks, which model systems that vary over time