Search engines and web domains have been around since the beginning of the internet. Despite being the most popular search engine now, Google has been around for a very short period of time. Here is how this search engine conglomerate was founded!
The beginning
Like all love stories, Larry Page’s and Sergey Brin’s story had very troubled beginnings. The two met for the first time at Stanford University in the year 1995 and from the moment they locked eyes, everyone around knew trouble would follow. At the time, Brin was a second year student, whose job was to take potential students around campus and Page, an interested student, ended up in Brin’s group.
Walking up and down the hill, Brin and Page could not stop arguing with each other, constantly debating and fighting over everything from the weather to the historic places of San Francisco. Obnoxious, social and extremely opinionated, Page and Brin were poles apart in every way. What made them come together then? Their interests and desire to create a one of a kind search engine.
The first project
Page and Brin were thrown together for the first time ever when they started writing the code for a search engine, then called BackRub, because of its ability to analyse backlinks. Despite the tumultuous relationship shared by the co writers, the code became a massive success. So successful was the project that it resulted in the creation of a research paper titled The Anatomy of a Large Scale Hypertextual Web Search Engine.
BackRub was unique in its functionality. By using an internally developed technology called PageRank, BackRub ranked a website’s importance by taking into account the number of pages on the website, the importance of the pages and the number of times they were linked to to the original site. The product was relatively successful but unfortunately, buyers were not interested in getting a product which was still in its initial stages. Finally, after multiple rejections. Brin and Page decided to innovate and create something new with (hold your breaths) Google!
The name that changed the world
Page and Brin took inspiration for the name Google from Gogol and voila, the strategy worked! A new name and a new beginning was all the founders needed for Google to get the much needed investors. Sun Microsystems co founder Andy Bechtolsheim was so impressed that after a quick demo of Google, he told the pair “Instead of us discussing all the details, why don’t I just write you a check?”
Bechtolsheim’s check was for $ 100,000 (made out to Google Inc) giving Page and Brin the much needed push toward success. The first two weeks after the founders got the check was quite an iconic one. With more than enough money in the bank, the duo opened their first office in Menlo Park, California. Post that, Google.com, a search engine that answered more than 10,000 questions a day, was launched.
History was created
Four years later, Page and Brin dropped the word beta from Google’s title and in 2001, Google finally filed and received a patent for its Ranking Technology! After that, there was no stopping Google and ever since, the search engine has changed the way the internet works. Interestingly, the first ever Google Doodle of the Burning Man was created to talk about the company’s motto, “Do No Evil.”
Google has come a long way, from being an accidental company to being one of the largest in the world. With exquisite patents to its name and extensive research at its hands, Google has definitely created a place for itself in the world of technology and innovation.
Microsoft has made significant strides in the field of AI with the release of its new Phi-3.5 models. This series includes Phi-3.5-MoE-instruct, Phi-3.5-mini-instruct, and Phi-3.5-vision-instruct, which demonstrate impressive performance, surpassing industry benchmarks and rivaling models from leading AI companies like OpenAI, Google, and Meta.
Key Highlights of the Phi-3.5 Models
Phi-3.5-MoE-instruct: This powerful model features 41.9 billion parameters, excelling in advanced reasoning tasks and outperforming larger models such as Llama 3.1 and Gemini 1.5 Flash. It supports multilingual capabilities and can process longer context lengths, making it versatile for various applications.
Phi-3.5-mini-instruct: A lightweight yet potent model with 3.8 billion parameters, it demonstrates strong performance in long-context tasks, outperforming larger models like Llama-3.1-8B-instruct and Mistral-Nemo-12B-instruct-2407. This model is optimized for quick reasoning tasks, making it ideal for applications such as code generation and logical problem-solving.
Phi-3.5-vision-instruct: With 4.15 billion parameters, this model excels in visual tasks, surpassing OpenAI’s GPT-4o on several benchmarks. It can understand and reason with images and videos, making it suitable for applications that require visual comprehension, such as summarizing video content or analyzing charts.
Open-Sourcing the Future of AI
Microsoft’s commitment to open-sourcing these models aligns with its vision of democratizing AI technology. By making these models available on Hugging Face under an MIT license, Microsoft empowers researchers and developers to build innovative AI applications without the constraints typically associated with proprietary software.
The Phi-3.5 models have the potential to revolutionize various industries, including healthcare, finance, and education. Their advanced capabilities can help automate tasks, improve decision-making processes, and enhance user experiences across different platforms.
Advanced Features
One of the standout features of the Phi-3.5 series is its extensive context window of 128,000 tokens, which allows the models to process large amounts of data effectively. This capability is crucial for real-world applications that involve lengthy documents or complex conversations, enabling the models to maintain coherence over extended interactions.
The training process for these models was rigorous:
The Phi-3.5-mini-instruct was trained on 3.4 trillion tokens over a span of ten days.
The Phi-3.5-MoE-instruct required more extensive training, processing 4.9 trillion tokens over 23 days.
The Phi-3.5-vision-instruct was trained on 500 billion tokens using a smaller training period of six days.
These extensive training datasets comprised high-quality, reasoning-dense public data that enhanced the models’ performance across numerous benchmarks.
Conclusion
As AI continues to evolve, Microsoft’s Phi-3.5 models are poised to play a crucial role in shaping the future of technology by offering smaller yet highly efficient solutions that outperform larger counterparts in specific tasks. By focusing on efficiency and accessibility through open-source initiatives, Microsoft is addressing the growing demand for powerful AI tools that can be deployed in resource-constrained environments as well as large-scale cloud settings.
The introduction of these models not only signifies a leap forward in AI capabilities but also challenges traditional notions about model size versus performance in the industry, potentially paving the way for more sustainable AI development practices in the future.
Apple is revolutionizing the way we create music and podcasts with a groundbreaking update to the Voice Memos app on the iPhone 16 Pro series. The introduction of AI-powered layered audio recording in the iOS 18.2 update allows users to effortlessly combine multiple audio tracks directly on their iPhones, making it an invaluable tool for musicians, podcasters, and content creators.
Key Features of Layered Recordings
The new Layered Recordings feature enables users to:
Record Vocals Over Instrumental Tracks: Users can play their music through the iPhone’s speakers while simultaneously recording their voice. This feature allows for capturing professional-quality audio without the need for external equipment, making it highly accessible for creators on the go.
Create Complex Audio Projects: The ability to layer multiple tracks of vocals, instruments, and sound effects empowers users to build intricate compositions directly on their devices.
Edit and Mix Audio: Advanced editing tools are available within the app, allowing users to fine-tune their recordings and apply professional-grade effects. This makes Voice Memos a powerful alternative to traditional studio setups.
Advanced Technology Behind the Feature
Powered by the A18 Pro chip and advanced machine learning algorithms, Voice Memos can intelligently isolate vocals from background noise, ensuring crystal-clear recordings. This technological advancement enhances the quality of audio captured, making it suitable for professional use.
Apple has showcased this feature with the popular Christmas song “Maybe This Christmas,” recorded by Grammy Award winners Michael Bublé and Carly Pearce, highlighting the practical applications of Layered Recordings in real-world scenarios.
Exclusive Availability
Currently, this powerful tool is exclusive to the iPhone 16 Pro and iPhone 16 Pro Max, emphasizing Apple’s commitment to pushing the boundaries of mobile creativity. The app’s capabilities are designed specifically for these models, leveraging their superior hardware to deliver enhanced performance. Users on other models, including the base iPhone 16 or iPhone 16 Plus, will not have access to this feature due to hardware limitations.
Broader Implications for Content Creation
The upgrade to Voice Memos represents a significant shift in how content creators can work. By enabling high-quality recording directly on their devices, Apple is catering to a growing demographic of musicians and podcasters who require flexibility and efficiency in their creative processes. This update not only enhances productivity but also democratizes access to high-quality audio recording tools.
Conclusion
With the introduction of AI-powered layered recording in Voice Memos on the iPhone 16 Pro series, Apple has set a new standard for mobile audio production. The combination of advanced technology, user-friendly features, and professional-grade capabilities positions Voice Memos as an essential tool for anyone looking to create music or podcasts on the go. As AI technology continues to evolve, we can expect even more exciting advancements that will further empower creators in their artistic endeavors.
YouTube is taking a significant step in breaking down language barriers by expanding its AI-powered auto-dubbing feature to knowledge and information-based channels. Initially introduced at VidCon 2022, this feature leverages Google’s Aloud technology to automatically translate and dub videos into multiple languages, enhancing accessibility for creators and viewers alike.
How it Works
Automatic Detection and Dubbing: YouTube’s AI automatically detects the language of uploaded videos and generates dubbed versions in supported languages. This process is seamless for creators, who can upload their content without needing to make additional adjustments for dubbing.
Language Support: The auto-dubbing feature currently supports translations between several languages, including English, French, German, Hindi, Indonesian, Italian, Japanese, Portuguese, and Spanish. This wide range of languages allows creators to reach diverse audiences across different regions.
Creator Control: Creators have the flexibility to review the auto-dubbed versions before they are published. They can choose to approve, unpublish, or delete these versions as they see fit, ensuring that the final content aligns with their standards.
Impact on Educational Content
This expansion aims to significantly increase the reach of educational and informative content to a global audience. By making videos accessible to viewers who speak different languages, YouTube empowers creators to share their knowledge and insights with a wider audience. For instance, a cooking tutorial originally in English can now be enjoyed by non-English speakers in countries like France or Japan.
Limitations and Future Improvements
While the technology presents exciting opportunities, there are some limitations:
Naturalness of Dubs: Currently, the auto-dubbed voices may not always sound entirely natural or convey the original tone and emotion of the speaker. YouTube acknowledges that this technology is still evolving and may not always produce perfect results.
Translation Accuracy: There may be instances where translations fall short or do not accurately represent the original content’s intent. YouTube is actively working on improving the accuracy and expressiveness of the auto-dubbed audio tracks.
YouTube has committed to ongoing enhancements, including an upcoming update called “Expressive Speech,” which aims to replicate not only the spoken content but also the creator’s tone, emotions, and environmental ambiance. This improvement will help create a more authentic viewing experience for users worldwide.
Conclusion
As YouTube expands its AI-powered auto-dubbing feature to more knowledge-focused channels, it is poised to make a substantial impact on content accessibility across the platform. By breaking down language barriers, YouTube is enabling creators to connect with audiences globally, fostering a more inclusive environment for learning and sharing information. As this feature continues to develop, it represents a significant advancement in how educational content can be consumed across different cultures and languages.