The most revolutionary technology that is developing right now is AI. Artificial Intelligence enables individuals, companies, and communities to reach their full potential by assisting in the early diagnosis of illnesses and allowing users to access information in their native tongue. And it creates new possibilities that have the potential to enhance the lives of billions of people significantly. In order to fulfill their objective of organizing the world's knowledge and making it widely accessible and valuable, they reoriented the firm around AI six years ago. As a result, they consider AI to be the most effective method we can use to accomplish this mission.
Since then, we have made extensive investments in AI, and organizations like Google AI and DeepMind are leading the way. The most complex AI computations are doubling in size every six months, significantly outpacing Moore's Law. At the same time, advanced generative AI and large language models are drawing interest from individuals all around the world. Indeed, many of the practical AI applications you are starting to see now are based on the findings of our Transformer research project, our 2017 field-defining article, and our notable diffusion model breakthroughs.
Working on these technologies at this time is exciting as we turn thorough research and technological advances into goods that genuinely benefit people. That has been our experience with big language models. Two years ago, we presented our Language Model for Dialogue Applications, which powers the newest generation of language and conversation capabilities (or LaMDA for short).
We have been developing Bard, an experimental LaMDA-powered conversation AI service. And while we prepare to make it more broadly accessible to the public in the coming weeks, we are moving forward by offering reliable testers today.
Bard focuses on bringing together the depth of human knowledge with our massive language models' strength, humor, and inventiveness. It uses data from the internet to provide original, excellent answers. Bard may serve as a creative release and a springboard for inquiry, enabling you to impart new scientific findings from NASA's James Webb Space Telescope to a 9-year-old or learn more about the top football strikers of the moment before receiving training to hone your abilities.
When describing new findings from NASA's James Webb Space Telescope to a 9-year-old, use Bard to demystify complex subjects.
We are first making it available using LaMDA's lightweight variant. We can grow to more people and get more input since our simpler model uses much less computer power. We will mix external input with our internal testing to ensure that Bard's replies reach a high standard for quality, safety, and groundedness in real-world knowledge. We are eager to use this testing period to continue learning and enhancing Bard's performance.
Bard has been added for the benefit of the users. As previously, customers may get complex information in plain English. In addition, Google's chatbot can provide consumers with current and accurate information. The new chatbot from Google is considered a rival to ChatGPT since it was developed and unveiled quickly.
The launch of Bard, an AI-based chatbot, has also been announced by Google, which is widely recognized for dominating the search engine business. Discovering the most current, correct replies will inform users and demonstrate how Google's most recent AI technology can educate users on recent events. ChatGPT often only delivers the information with accuracy up to that data since it was only trained on data until 2021.
Bard is powered by LaMDA (Language Model for Dialogue Applications), a Google convolution neural language model. Currently, Google is offering the Bard with a LaMDA variant that is more lightweight. This is so that Bard can reach more consumers and get more feedback. Smaller models often need less computational power. To ensure that the quality of the responses it gets from Bard is kept to a high level and is based on real-world facts, Google will combine the feedback it receives from external users with its internal testing.
We've used AI to improve Search for billions of people for a long time. BERT, one of our first Transformer models, had a ground-breaking capacity to understand spoken language's subtleties. MUM, released two years ago, has next-level and multilingual information comprehension and is 1,000 times more effective than BERT. It is 1,000 times more effective than BERT and can recognize significant points in videos. It also offers essential information in additional languages, such as crisis help.
LaMDA, PaLM, Imagen, and MusicLM, some of our most recent AI inventions, are building on this by creating new ways to engage with information, including language, images, videos, and audio. We are attempting to incorporate these most current advancements in AI into our products, starting with Search. One of the most exciting possibilities is how AI might enhance human comprehension of information and more effectively transform it into valuable knowledge, making it more straightforward for people to find what they're searching for and complete tasks. People often envision coming to us for fast, factual responses like "how many keys does a piano have" when they think about Google. However, more and more individuals are looking to Google for more profound knowledge and insights, asking questions like "Is it simpler to learn the piano than the guitar, and how much practice does each require?" Finding out what you genuinely need to know about a subject like this may take a lot of work, and individuals often want to consider several points of view.
When there is no correct solution to a subject, AI may be helpful in synthesizing findings. Soon, you will notice AI-powered Search features that condense complex information and multiple viewpoints into digestible formats to quickly understand the big picture and learn more from the web, whether this means looking for additional perspectives, like blogs from people who play both the piano and the guitar or going deeper on a related topic, like beginner-friendly starting points. Soon, Google Search will begin implementing these new AI capabilities.
A search result for "Is piano or guitar simpler to learn and how much practice does each require" is shown on a mobile device. Using AI, the search result page presents the response to the query. In order to help you understand the larger picture while seeking insights, AI capabilities in Search may condense information.
For others to benefit from these breakthroughs, we must make it easy, secure, and scalable for them to do so by allowing them to build on top of our best models and our products. Next month, they will start accepting applications from independent programmers, artists, and companies that want to test our generative language API, which is initially powered by LaMDA and will later use various models. In order to make it easier for others to create more cutting-edge AI apps, we plan to offer a set of tools and APIs.
For startups to build reliable AI systems, they need computational power. Because of this, we are delighted to assist the scalability of these programmes via our Google Cloud contracts with Cohere, C3.ai, and Anthropic, which were just announced last week; keep an eye on this area for more developer information soon. When exposing experiences based on these models to the outside world, we must do it fearlessly and ethically. Because of this, we are committed to creating AI safely:
In 2018, Google was one of the first companies to publish a set of AI Principles.
They continue to engage with communities and subject-matter experts, work with governments and other organizations to establish standards and best practices, and provide education and tools for our researchers in order to make AI safe and valuable.
They will continue to be brave with innovation and prudent in our approach, whether using AI to fundamentally improve our products or making these potent capabilities accessible to others. And it's just the beginning; in the following weeks and months, further developments will be in each of these areas.
Dairy Delivery Software
Native Milk always had to deal with route optimization to maximize profit and frequent changing of delivery boys. While this was easily manageable manually in offline business, it was a big headache when their business went digital.
We developed a mobile application for the drivers with route optimization. The total delivery area is divided into various routes and orders are sorted according to different routes. A driver is then assigned a single route.
PlanTech, (Construction Project Management software Case Study) a well-established US based construction company with huge projects, needed an improvement of their business processes and better optimization of their resources. As their business expanded, projects continuously got delayed which resulted in lawsuits by unhappy clients. We faced a variety of challenges to develop niche technological solutions for a company involved in the construction industry.
We designed separate web and mobile applications for foremen i.e. project managers and for construction staff (labourers). The applications has synchronized features such as calendar, attendance etc. that were updated real time Simple user interface for the layman was developed after many iterations and approval from the client.
We developed a dedicated central project management panel. The purpose was to have a centralised system update where all team leaders from different departments could update on their tasks at work.
We needed a fast technology to synchronize the source data to our system.
We had to run our programs real time during the horse race.
We ran our calculations on the given data, but by the time we generated an analysis of the probable winner, the market trends and positions of the race horses altered so our result had no value.
The reason behind using node.js is that we were able to establish a swift and persistent connection between betfair and our algorithm.
We could run our program with race data and generate results using the latest trends up-to-the-second with more than 99 % of the source data matching with our index.
On-Demand Delivery App
The client was clear with their needs but was not tech savvy enough to state specific requirements.
In GetIt the driver and vendor are treated as separate entities unlike a regular taxi application where such division does not exist.
We initiated brainstorming sessions with the client to plan out the project.
We built application functionality to optimize the process while simultaneously taking all stakeholders into account.
The client had an understanding of NFC but they had no idea of how the technology behind NFC works.
The client needed a secure platform as they wanted to keep the customer data secured.
We did a knowledge transfer with the client and explained the technology behind NFC and QR code.
We undertook a variety of security measures to prevent any leakage of user data.
The oldest insurance company of Lebanon reached out to us. They needed to digitize their operations and develop a lending application.
The target customers of the bank were mostly laymen with not much technical know-how. They consisted of old people, rural families, middle class urban families etc. Thus digitization was a challenge.
We developed a mechanism for the bank to directly disburse the loan amount to the customer’s bank account. This saved a trip to the bank of the customer and saved a lot of time for both the parties.