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For a long time, engineers and scientists sought to make artificial intelligence (AI) perform like the human brain. This feat became feasible with the creation of Google Brain, an AI research team, in 2011. So what does Google Brain entail, and what are its advancements and breakthroughs in AI?

The human brain is likely the most complex creationan intricate biological machine with many areas simultaneously performing different tasks. However, AI developers aim to make AI systems perform complex operations and solve problems like humans.

In 2011, Andrew Ng, a college professor, Jeff Dean, a Google fellow, and Greg Corrado, a Google Researcher, established Google Brain as a research team for exploring AI.

Initially, the team didn't have an official name; after Ng joined Google X, he began collaborating with Dean and Corrado to integrate deep learning processes into Google's existing infrastructure. Eventually, the team became a part of Google Research and was called "Google Brain."

The founding Brain team members sought to create intelligence that could independently learn from large amounts of data. They also aimed to address existing AI networks' challenges, including language understanding, speech, and image recognition.

In 2012, Google Brain encountered a breakthrough. The researchers fed millions of images obtained from YouTube into the neural network to train it on pattern recognition without prior information. After the experiment, the network recognized cats with a high degree of accuracy. This breakthrough paved the way for a wide range of applications.

Google Brain revolutionized how software engineers thought of AI, contributing significantly to its development. The Brain team has achieved tremendous results in many machine learning operationsits successes formed the foundation for AI's speech and image recognition and natural language processing.

One of the Brain team's most important contributions is the development of deep learning and the progression of Natural Language Processing (NLP).

NLP involves teaching computers human languages and helping them interact, delivering improved results with continued exposure. For instance, Google Assistant uses NLP to understand your queries and respond appropriately.

The Brain team has contributed to Computer Visionidentifying pictures and objects from visual data. In 2012, Google Brain introduced a neural network to classify images into 1000 categories. Presently, there are several unexpected uses for Computer Vision in use right now.

Google Brain also developed Neural Machine Translation (NMT). Before the introduction of the Brain team, most translation systems used statistical methods; Google's Neural Machine Translation was a significant upgrade.

The system translates whole sentences at once, resulting in more accurate translations that sound natural. Google Brain has also developed network models that can accurately transcribe speech.

The Brain team has pioneered a host of Google applications since its inception in 2011, including the following.

The Google Assistant, found in many smartphones today, provides personalized information, helps you set reminders and alarms, makes calls to various contacts, and even controls smart devices around the home.

This assistant relies on the machine learning algorithms provided by Google Brain to interpret speech and give an accurate response. With these algorithms, Google Assistant makes your life easier by learning your preferences and, after prolonged usage, understands you even better.

The Google Translate system uses Neural Machine Translation, which employs deep learning algorithms from Google Brain. This allows Google Translate to identify, understand, and accurately translate the text into the desired language.

NMT also uses a "sequence-to-sequence" modeling approach. This means phrases and whole sentences are translated in one go rather than word by word. Over time, as you interact with Google Translate, it gathers information, which allows it to provide more natural-sounding translations in the future.

If you need more insight, check out how to translate audio with Google Translate on your android phone.

While Google Photos is primarily a cloud-based photo and video storage application, it uses Google Brain's algorithms to organize and categorize media automatically. This lets Google Photos make it easier for you to manage your stored pictures. So, when you take a picture, Google Photos recognizes you, your friends, objects, and even landmarks and events present in the photo.

The application also adds tags to help you group the picture for future reference. This feature is particularly useful for finding and sharing memories with friends later.

Google Brain, since its inception, has dramatically expanded AI using top-notch neural network algorithms. The Brain team has contributed to speech and image recognition breakthroughs, machine learning frameworks, and natural language processing.

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What Is Google Brain, and What Is Its Role in Artificial Intelligence ... - MUO - MakeUseOf

Original Article published in March/April 2023 Power Torque

BY WARREN CLARK*

Most of us are hearing a lot about Artificial Intelligence and the prospect of it replacing humans in many types of work.

Artificial Intelligence thats AI for short is the technology that enables machines (such as robots and computers) to think and act like humans.

And ChatGPT is an AI website that uses information it has learned to generate text in response to questions.

I asked it to write my column for this month and started by asking about Deals on Wheels:

When it comes to trucking, safety is a top priority. Even when it comes to saving money, you dont want to compromise safety. Here at Deals on Wheels, we understand this and strive to give our readers the best deals while still making sure theyre getting the highest quality of safety and reliability. For starters, we always look for the best deals on trucks, parts, and accessories. We make sure to only feature those that are from reputable manufacturers and are designed for the safe operation of a truckAt Deals on Wheels, safety is our top priority. We want to provide our readers with the best deals while still making sure theyre getting the highest quality of safety and reliability.

So far, so good.

I was on a roll.

It did get me thinking about the enormous impact technology is already having on our industry.

AI is already here in the heavy vehicle sector. ChatGPT told me so:

AI has been used in the trucking industry for many years, and its uses are becoming increasingly more sophisticated. AI can be used to automate navigation, loading, and unloading, as well as other tasks. It can also be used to optimize routes, reduce fuel costs, and minimize downtime. AI can also be used in truck maintenance and safety, as well as in predictive maintenance, which can save costs in the long run. AI can also be used to analyse data to improve customer service and provide customers with better insights into their operations. AI is also being used for driverless trucks, which could potentially reduce accidents, reduce traffic congestion, and improve efficiency.

Driverless trains are already here on the Sydney Metro. The New South Wales government has trialled an autonomous bus to run along a simple Point A to Point B route at Homebush Bay in Sydney.

Since 2021, Australian company Mineral Resources has been piloting an autonomous triple road train platoon in the Pilbara, with each combination capable of hauling 300 tonnes of iron ore.

The National Transport Commission has been developing a regulatory framework for some self-driving vehicles. The Automated Vehicle Safety Law is due in 2026 and is expected to lay out national standards for autonomous vehicles.

You might expect this will result in sweeping changes to how road transport operates.

Not so fast.

American researchers at two universities have been assessing the impact of autonomous trucking on operator hours. They say there will be a profound impact on long-haul trucking but it may take decades to come about.

In March last year, an autonomous 18-wheel truck spent more than five successive days hauling goods between Dallas and Atlanta, running around the clock to cover multiple journeys totalling 10,000 kilometres.

The same freight task would have taken a human driver (with mandated rest breaks) about 10 days.

Critically, a human safety driver was in the cab at all timesand had to take the wheel many times.

It seems AI is up to the task when its straightforward but has a lot to learn when conditions are tough. Another study highlights its issues in built-up environments where the sun doesnt shine so much.

Anyway, I was emboldened enough to ask ChatGPT how long before AI replaces my role:

It is difficult to predict exactly when a CEO of a lobby group will be replaced by AI, as it depends on a variety of factors, including the specific organization and its goals, the level of technological development in the field of AI, and the acceptance and adoption of AI within the industry. However, it is possible that in the future, AI could play a larger role in the operations of lobby groups, such as in the analysis of data and the development of strategies.

ChatGPT is unlikely to be able to attend meetings with Ministers any time soon, so I reckon my job is safe.

If youre a competent heavy vehicle operator whose skills are very much in demand right now, I suggest yours is too.

AI clearly has a role to play in many facets of road transport especially in route planning and management of bulk freight. Used properly, its going to make everybodys job easier.

But humans with skills, finely honed by years of experience, arent going to vacate the scene any time soon.

And remember: governments at all levels are still trying to work out how to get us into electric trucks.

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Artificial Intelligence isn't about to make us all jobless just yet NRTA - NatRoad

On 16 March, Microsoft announced its latest artificial intelligence (AI) integration with Microsoft 365 Copilot. Focused on workplace productivity, Copilot combines the power of large language models with business data and the Microsoft 365 apps to unleash creativity, unlock productivity and uplevel skills," according to a press statement.

Also read: All you need to know about Googles new AI features and GPT-4

SIMILAR STORIES

There are two main ways to use the GPT-4 powered Copilot, which is designed to work like an assistant. First, it is integrated with everyday apps such as Word, Excel, PowerPoint, Outlook, and Teams so users can access it easily. Second, Microsoft has also launched Business Chat. It works alongside your Microsoft 365 apps and data such as calendar emails, chats, documents, meetings, and contacts to enhance the experience and make it easier and faster.

For instance, you can use prompts such as tell your team how you updated a product strategy" and it will generate an update based on the mornings meetings, emails, and chat threads.

With our new copilot for work, were giving people more agency and making technology more accessible through the most universal interface natural language," Satya Nadella, Chairman and CEO of Microsoft said in a statement.

According to Microsoft, Copilot will fundamentally transform the way we work" and will turn your words into the most powerful productivity tool on the planet.

Copilot has been built to free up space and help people focus better on the tasks at hand, according to the statement. In Word, it writes, summarises and creates along with the user. It can give you a draft to edit or use as a starting point. Although Microsoft acknowledges Copilot might not always be right, it will help you save sourcing, writing, and editing time.

In PowerPoint, you can create vivid presentations with a single prompt and then add relevant content. In Excel, Copilot will help create data visualizations in a matter of seconds and identify trends.

Now, clearing your inbox won't leave you drained as Copilot can do that for you in minutes and help you manage it so that you can focus on the communication part. With the Copilot in Power Platform, you can automate repetitive tasks, create chatbots and turn an idea into a working app in minutes, according to the statement.

Microsoft is currently testing Copilot with select commercial customers.

Also read: Explained: Discord announces AI chatbot Clyde and more

Read the rest here:
Microsoft brings artificial intelligence tool Copilot to Word, Teams - Mint Lounge

Machines might not be ready to replace humans just yet, but there's no escaping the fact that they're playing a growing role in certain fields. Design is one of them.

The sector is changing rapidly with the advent of so-called "generative" artificial intelligence, which designs objects based on textual descriptions.

This technology allows anyone to slip into the shoes of a designer and imagine products that could become part of our daily lives.

Eric Groza has tried his hand at this. The Dubai-based creative director decided to entrust the design of a fictional collaboration between the Swedish furniture giant Ikea and the California-based outdoor clothing brand Patagonia, to Midjourney, an artificial intelligence image generation programme.

He shared the results of his experiment on LinkedIn, in a post that spurred nearly 43,000 reactions. It features fictive creations such as camping chairs, an outdoor sofa and lamps that combine the visual identities of the two companies.

To come up with this capsule collection, Groza launched the generation of 200 different images in Midjourney. In the end, he selected just eight that he believes illustrate both brands' commitment to sustainability and environmental protection.

"For Ikea (this collaboration) would push the boundaries of their furniture to a new frontier. For Patagonia, it would showcase how their sustainable multifunctional comfort can be used indoors as well," he explained.

But creating this imaginary collection was not so straight-forward for the creative director. The first images generated by Midjourney were not very convincing because Groza had not yet mastered the art of the prompt, ie, the ability to formulate instructions sent to the artificial intelligence tool to enable it to create a visual from scratch.

The image generation software that has flooded onto the Internet in recent months, including DALL-E 2, Imagen, DreamBooth and Stable Diffusion, works on the basis of keywords and textual descriptions.

They use language understanding and learning models on very large amounts of data to design images from any written request.

But you still have to know how to formulate these requests correctly. And that's exactly where the challenge lies in using generative artificial intelligence.

Progress that raises questions

However, once the right prompt is found, the machine does all the work.

"Its magic, but requires someone at the controls to hold its hand," said Groza. The images of the Ikea/Pantagonia collaboration quickly went viral on the professional social network, where they drew mixed reactions.

Some praised the technological feat, while others objected to the use of image-generating software in the design industry. The latter fear that some of the work usually done by designers will be delegated to AI, a concern shared by many professionals in the creative industries.

The design industry has been grappling with these questions for years, even if generative artificial intelligence programmes are relatively recent.

In 2018, the US software company Autodesk began offering an "iterative design" service within its Netfabb Ultimate product design software.

Here, the user specifies requirements such as the material used or the production method, which the machine then takes into consideration to come up with ideas.

The user can then choose from the options available, or adapt the request until the desired result is obtained.

At the time, Jean-Louis Frechin, president of the Paris-based design agency NoDesign, said that the arrival of such tools would lead to a rethinking of the role of designers.

"This is an important evolution and design must seize this technology, because we don't see great human progress without new tools," he told the French financial newspaper Les Echos.

"The only danger is that engineers may imagine that they can model the creation with this always lingering idea of getting rid of designers."

Artificial intelligence is already being used to design many objects. Nasa researchers have used it to design optimised parts for various projects, including the Mars Sample Return space mission, which will bring back to Earth soil samples taken from the Red Planet by the Perseverance rover.

These metal structures have the advantage of being much lighter than those usually designed by humans and of supporting higher structural loads. They are also designed much more quickly, which significantly reduces manufacturing time.

"You can perform the design, analysis and fabrication of a prototype part, and have it in hand in as little as one week," explains Ryan McClelland, a research engineer at the Goddard Space Flight Center, in a statement. "It can be radically fast compared with how were used to working."

But, make no mistake, the designer's expertise remains essential to achieving a satisfactory final result.

"Human intuition knows what looks right, but left to itself, the algorithm can sometimes make structures too thin," said M. McClelland.

He adds: "The algorithms do need a human eye." AFP Relaxnews

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Designers' dilemma: Embrace or evade the advancements of ... - The Star Online

Lemken and Steketee have introduced an AI-enhanced version of their automatic intra-row hoeing machine to the market.

Hoeing technology becomes even more effective if the machine itself is able to distinguish between crop plants and weeds and works in a targeted manner as a result. This is where artificial intelligence (AI) comes into play. Lemken and Steketee are now introducing the automatic intra-row hoeing machine IC-Weeder in an AI-enhanced version which reliably detects crop plants. This machine is therefore able to create clean fields even in sown crops with extensive weed infestation within rows.

To achieve the goal of autonomous plant recognition, the software integrated into the IC-Weeder AI first needed to learn certain features of crop plants and then combine them to produce complex contexts in a second step. This is possible thanks to an algorithm based on the principle of deep learning. For this particular machine, sugar beet plants were manually marked up at various stages of development. The algorithm then used this data to autonomously create a method for identifying sugar beet plants based on their colour profile, texture, shape, size and leaf position. This allows the hoeing machine to work even in challenging conditions that are too complex for conventional image recognition systems, as the system is able to differentiate clearly between crop plants and weeds.

In the Steketee IC-Weeder, the cameras for the individual rows are well protected and located inside a casing to ensure their reliable function without being affected byenvironmental light conditions. During a pass with the IC-Weeder AI, the cameras transmit 30 images per second to the on-board computer, producing a plant recognition ratio in excess of 95%. The sickle- shaped, pneumatically controlled knives move into the rows and hoe as close as two centimetres from each plant. A hydraulic parallel sliding frame ensures precise and reliable machine control in the crop.

With the Steketee IC-Weeder AI, Lemken aims to make hoeing even more precise and effortless. At the same time, this smart tillage implement prepares the path towards fully automated driving systems. Lemken UK will have IC-WeederAI machines available to demonstrate in spring 2023, initially for sugar beet and all other transplanted vegetable and salad crops with in row weeding capability in a working width of three metres.

Click on the video below to watch a short clip showing the IC-Weeder in action.

Link:
Autonomous hoeing thanks to artificial intelligence - Farmers Guide