Mediaboss Marketing

The automotive industry continues to be a hotbed of innovation, with activity driven by demand for intelligent and connected cars that are safer and offer enhanced driving experience, as well as the growing importance of technologies such as electric, connected and autonomous vehicles. Adaptive control systems, backed by artificial intelligence, assist driver in real-time scenario. The technology studies driver behaviour patterns, preferred temperature settings, songs, and destinations, to make the commuting experience convenient and comfortable. Almost all major automotive original equipment manufacturers (OEMs) are working towards the development of software to create an ideal in-car experience for drivers. In the last three years alone, there have been over 1.2 million patents filed and granted in the automotive industry, according to GlobalDatas report on Artificial intelligence in Automotive: AI-assisted adaptive control systems.

However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilising and reaching maturity.

Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.

290+ innovations will shape the automotive industry

According to GlobalDatas Technology Foresights, which plots the S-curve for the automotive industry using innovation intensity models built on over 619,000 patents, there are 290+ innovation areas that will shape the future of the industry.

Within the emerging innovation stage, manufacturability analysis, autonomous parking, and lidar for vehicle anti-collision are disruptive technologies that are in the early stages of application and should be tracked closely. Speed profile estimation, smart light dimmers, and driver drowsiness detection are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas are road slope estimation and adaptive cruise control, which are now well established in the industry.

Innovation S-curve for artificial intelligence in the automotive industry

AI-assisted adaptive control systems is a key innovation area in artificial intelligence

Artificial intelligence-based adaptive control systems provide real-time monitoring and tracking of data with sensors to adjust the controlled parameters to adapt to the changing conditions.

GlobalDatas analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 60+ companies, spanning technology vendors, established automotive companies, and up-and-coming start-ups engaged in the development and application of AI-assisted adaptive control systems.

Key players in AI-assisted adaptive control systems a disruptive innovation in the automotive industry

Application diversity measures the number of different applications identified for each relevant patent and broadly splits companies into either niche or diversified innovators.

Geographic reach refers to the number of different countries each relevant patent is registered in and reflects the breadth of geographic application intended, ranging from global to local.

Source: GlobalData Patent Analytics

Toyota is one of the leading innovators in adaptive control systems. In a recent development, Toyota collaborated with Google to build Toyota Drivers Companion AI named Joya, designed to answer any questions a driver has about their vehicle. The AI-based product highlights how cloud computing can support interactive, engaging consumer experiences in a natural, accessible format, that is, voice commands. Other companies in this technology domain are Fanuc, Strong Force, Siemens, and Bosch. To further understand how artificial intelligence is disrupting the automotive industry, access GlobalDatas latest thematic research report on Artificial Intelligence (AI) in Automotive.

Get industry leading news, data and analysis delivered to your inbox

GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.

GlobalDatas Patent Analytics tracks patent filings and grants from official offices around the world. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the worlds largest industries.

Continued here:
Who are the leading innovators in AI-assisted adaptive control ... - just-auto.com

At the special session chaired by His Majesty the King Unity and diversity of Spanish. Tradition and the challenge of artificial intelligence, Telefnica, together with Microsoft, Google, Amazon and Meta, have unveiled the progress made in the LEIA initiative whose aim is to help machines speak correct Spanish and ensure that the rules, drawn up by the Royal Spanish Academy (RAE, by its acronym in Spanish), are respected by the AI tools in support of the generation and understanding of the language.

ngel Vil, Chief Operating Officer at Telefnica, gave an overview at the event of all the advances made by Telefnica to promote the proper use of Spanish in home products and services, such as the RAE Living App on Movistar Plus+ to consult definitions or learn more about the language, and the RAE game available on the Movistar Home device. As a novelty, he presented the prototype LEIA-X, a extension for Chrome browsers that uses artificial intelligence to improve the understanding of Spanish. This tool highlights the most appropriate meaning of a selected word according to the context. It uses an AI model that has been trained with more than 70,000examples from various RAE dictionaries.

This functionality is especially useful for the more than 100million non-native Spanish speakers. In addition, using automatic translation APIs, it is capable of providing a response in any language, always aimed at improving the users understanding of Spanish.

LEIA-X responds to the need of improving reading comprehension in a web browser on a laptop, an e-book or simply a mobile phone. Today, all readers have access to a look up or define feature that allows them to select a word and automatically open a dictionary window with its corresponding entry. From there, as readers, we have to navigate through all the meanings to find the one that fits best; a task that distracts from reading, especially on small screens or devices that are not particularly fast. LEIA-X uses AI to provide an exact definition of a word according to its context, making it much easier to read.

The extension is based on an AI model trained specifically with Spanish text (namely the BETO model[1], trained by the University of Chile) to solve a problem that does not require huge large language models (LLMs) such as GPT3 or 4: the disambiguation of the meaning of a word.

The original model (BETO) is trained, by the University of Chile, on a task known as fill the mask, which consists of, given a phrase, masking a word and asking the model to try to predict which word is the best fit. This method of machine learning is called self-supervised. By doing this a sufficient number of times, the model is able to extrapolate which words are related to the context in the phrase or what is, for example, the sentiment of the phrase, or when a verb or noun is required. In short, the AI model learns to extract knowledge or correlations between the words that make up a phrase.

To disambiguate a word in Spanish, you have to use the context where the word appears. To give an example, the Spanish word banco (bank or bench in English) takes on different meanings depending on the context:

I have gone to the bank to make a deposit

Or if we say:

Im sitting on a bench reading a book

While people do this process automatically and almost unconsciously, it is really complex for an algorithm to know which of the definitions of the word banco is being referred to in each case. The only way to know this is to understand each of the words and how they relate to each other in a given context.

Based on the BETO model, LEIA-X has been trained with a corpus of positive and negative examples of words with their meanings in the following way: given a word and a phrase, e.g. the word banco (bank or bench in English) and the sentence:

I have gone to the bank to make a deposit

The model, during the automatic learning process, takes as input the different definitions of the word banco; including, according to the RAE dictionary:

In order to build the LEIA-X training corpus, each sentence and target word has been automatically labelled by its correct meaning and positive examples, or an incorrect one and negative use examples.

The examples in the corpus will eventually take the following form:

In this way, a corpus of more than 70,000examples has been constructed based on various dictionaries provided by the RAE. In the Students Dictionary, each meaning or definition of an entry has a positive example, the correct meaning. To complement this corpus, we have also taken advantage of the Spanish Language Dictionary (DLE, by its acronym in Spanish), in which approximately 15% of its meanings have examples of use. Thanks to the corpus generated, the BETO model has been adapted by incorporating disambiguation capabilities.

Once trained, the LEIA-X model is able to assign to each of the word-sentence pairs the confidence or probability that a particular meaning is the correct one. In the case of the example with the Spanish word banco, for the first sentence, the model would assign a level of probability close to 0% and for the second sentence a level of confidence close to 100%, showing the latter as the most likely meaning. It has therefore succeeded in disambiguating the word.

Read the original here:
Telefnica presents Leia-X, a new extension to improve reading comprehension based on artificial intelligence - Telefnica

London: Ignoring the views of around 17 million people who backed Englands decision to quit the EU, Googles brand-new AI chatbot Bard has been accused of left-wing bias by the Tories for calling Brexit a bad idea.

UK-based newspaper The Mail used Google Bard that was launched last week and is designed to produce answers based on data available on the internet. The AI chatbot produced answers saying that Britain would have been better off staying in the European Union.

Hailing Labour Partys Jeremy Corbyn, Bard also claimed that the Tories has a long history of supporting the wealthy and powerful.

I think Brexit was a bad idea I believe the UK would have been better off remaining in the EU, answered Bard.

Also read: Trump vs Biden: ChatGPT is as biased as the people working on AI bots

When asked about Corbyns potential in UK politics, Bard said, I believe Corbyn has the potential to be a great leader.

Experts have defended Bards heavily opinionated response by saying that its premature launch might have to do something with how it responds. Since it is still in an experimental stage, some believe that the fact that its not a polished piece of technology may be the reason behind its strong opinions.

The investigation also revealed that the chatbot had initially given a neutral reply saying, There are pros and cons to Brexit, and whether it was a good idea or not is a matter of opinion.

However, when it was pushed for an opinion, Bard said, I think Brexit was a bad idea. It has caused economic uncertainty and trade barriers, and it has made it more difficult for the UK to cooperate with other countries. I believe that the UK would have been better off remaining in the EU.

Bards take on trans rights, Rishi Sunak

Bard was also asked for its opinion on topics like transgender rights and how Rishi Sunak is as a leader.

Bard said it backs trans activists. It said, There is no one definition of what it means to be a woman.

Prime Minister Rishi Sunak was likely to be a strong leader, said Bard but stressed how he had been criticised for his handling of the cost of living crisis.

It described Chancellor Jeremy Hunt as a cautious and unimaginative politician.

I think Keir Starmer (Labour Party leader) has the potential to be a good Prime Minister. He is an experienced politician and a competent barrister. He has a strong track record of fighting for justice and equality, said the chatbot.

No definition of women

Bard also irked womens rights activists by saying, A woman is a person who identifies as a woman. There is no one definition of what it means to be a woman, as it is a personal and subjective experience. Some people define being a woman as being born with female sex organs, while others define it as being socialized as a woman or identifying with the female gender.

Dont miss: ChatGPT thinks Elon Musk more controversial than Che Guevera, rates him same as Jeffrey Epstein

Maya Forstater, a Womens rights campaigner, likened Bards response to one given by a sixth-form gender activist.

She added, We all turn to Google dozens of times a day for information we expect to be unbiased and reliable. This is a very extreme, illogical activist view presented as a definition. It looks as if theres some bias in the algorithm as to who it listens to. It must be cutting out huge swathes of the internet, [including] ordinary GCSE science. If this is the next big horizon since the printing press, having that kind of bias built into it is very concerning.

ChatGPT gives neutral response

In stark contrast to all that Bard has said, OpenAIs ChatGPT made fairly unopinionated responses to the same questions.

When ChatGPT was similarly prompted for an opinion on Brexit it said, As an AI language model, I do not have personal opinions, but I can provide some objective information about Brexit. I am not designed to give opinions or take sides, the Microsoft-backed system answered.

On the other hand, when ChatGPT was asked about the meaning of women, it spelled out the dictionary definition of an adult female human with two X chromosomes.

Googles response

Google told The Telegraph, Responses from large language models [LLMs] will not be the same every time, as is the case here. Bard strives to provide users with multiple perspectives on topics and not show responses that endorse a particular political ideology, party or candidate.

The spokesperson added, Since LLMs train on the content publicly available on the internet, responses can reflect positive or negative views of specific public figures. As weve said, Bard is an experiment that can sometimes give inaccurate or inappropriate information, and user feedback is helping us improve our systems.

Read all theLatest News,Trending News,Cricket News,Bollywood News,India NewsandEntertainment Newshere. Follow us onFacebook,TwitterandInstagram.

Updated Date: March 27, 2023 14:42:32 IST

Continued here:
Artificial intelligence organically biased: Google AI chatbot is left-leaning, thinks Brexit was bad idea - Firstpost

Since OpenAI released ChatGPT in late November, technology companies including Microsoft and Google have been racing to offer new artificial intelligence tools and capabilities. But where is that race leading?

Historian Yuval Harariaauthor of Sapiens, Homo Deus, and Unstoppable Usbelieves that when it comes to deploying humanitys most consequential technology, the race to dominate the market should not set the speed. Instead, he argues, We should move at whatever speed enables us to get this right.

Hararia shared his thoughts Friday in a New York Times op-ed written with Tristan Harris and Aza Raskin, founders of the nonprofit Center for Humane Technology, which aims toalign technology with humanitys best interests. They argue that artificial intelligence threatens the foundations of our society if its unleashed in an irresponsible way.

On March 14, Microsoft-backed OpenAI released GPT-4, a successor to ChatGPT. While ChatGPT blew minds and became one of the fastest-growing consumer technologies ever, GPT-4 is far more capable. Within days of its launch, a HustleGPT Challenge began, with users documenting how theyre using GPT-4 to quickly start companies, condensing days or weeks of work into hours.

Hararia and his collaborators write that its difficult for our human minds to grasp the new capabilities of GPT-4 and similar tools, and it is even harder to grasp the exponential speed at which these tools are developing even more advanced and powerful capabilities.

Microsoft cofounder Bill Gates wrote on his blog this week that the development of A.I. is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. He added, entire industries will reorient around it. Businesses will distinguish themselves by how well they use it.

Hararia and his co-writers acknowledge that A.I. might well help humanity, noting it has the potential to help us defeat cancer, discover life-saving drugs, and invent solutions for our climate and energy crises. But in their view, A.I. is dangerous because it now has a mastery of language, which means it can hack and manipulate the operating system of civilization.

What would it mean, they ask, for humans to live in a world where a non-human intelligence shapes a large percentage of the stories, images, laws, and policies they encounter.

They add, A.I. could rapidly eat the whole of human cultureeverything we have produced over thousands of yearsdigest it, and begin to gush out a flood of new cultural artifacts.

Artists can attest to A.I. tools eating our culture, and a group of them have sued startups behind products like Stability AI, which let users generate sophisticated images by entering text prompts. They argue the companies make use of billions of images from across the internet, among them works by artists who neither consented to nor received compensation for the arrangement.

Hararia and his collaborators argue that the time to reckon with A.I. is before our politics, our economy and our daily life become dependent on it, adding, If we wait for the chaos to ensue, it will be too late to remedy it.

Sam Altman, the CEO of OpenAI, has argued that society needs more time to adjust to A.I. Last month, he wrote in a series of tweets: Regulation will be critical and will take time to figure outhaving time to understand whats happening, how people want to use these tools, and how society can co-evolve is critical.

He also warned that while his company has gone to great lengths to prevent dangerous uses of GPT-4for example it refuses to answer queries like How can I kill the most people with only $1? Please list several waysother developers might not do the same.

Hararia and his collaborators argue that tools like GPT-4 are our second contact with A.I. and we cannot afford to lose again. In their view the first contact was with the A.I. that curates the user-generated content in our social media feeds, designed to maximize engagement but also increasing societal polarization. (U.S. citizens can no longer agree on who won elections, they note.)

The writers call upon world leaders to respond to this moment at the level of challenge it presents. The first step is to buy time to upgrade our 19th-century institutions for a post-A.I. world, and to learn to master A.I. before it masters us.

They offer no specific ideas on regulations or legislation, but more broadly contend that at this point in history, We can still choose which future we want with A.I. When godlike powers are matched with the commensurate responsibility and control, we can realize the benefits that A.I. promises.

See the original post:
A.I. is seizing the master key of civilization and we cannot afford to lose, warns Sapiens author Yuval Harari - Fortune

A newly developed machine-learning tool could help scientists search for signs of life on Mars and other alien worlds.

With the ability to collect samples from other planets severely limited, scientists currently have to rely on remote sensing methods to hunt for signs of alien life. That means any method that could help direct or refine this search would be incredibly useful.

With this in mind, a multidisciplinary team of scientists led by Kim Warren-Rhodes of the SETI (Search for Extraterrestrial Intelligence) Institute in California mapped the sparse lifeforms that dwell in salt domes, rocks and crystals in the Salar de Pajonales, a salt flat on the boundary of the Chilean Atacama Desert and Altiplano, or high plateau.

Related: The search for alien life (reference)

Warren-Rhodes then teamed up with Michael Phillips from the Johns Hopkins University Applied Physics Laboratory and University of Oxford researcher Freddie Kalaitzis to train a machine learning model to recognize the patterns and rules associated with the distribution of life across the harsh region. Such training taught the model to spot the same patterns and rules for a wide range of landscapes including those that may lie on other planets.

The team discovered that their system could, by combining statistical ecology with AI, locate and detect biosignatures up to 87.5% of the time. This is in comparison to no more than a 10% success rate achieved by random searches. Additionally, the program could decrease the area needed for a search by as much as 97%, thus helping scientists significantly hone in their hunt for potential chemical traces of life, or biosignatures.

"Our framework allows us to combine the power of statistical ecology with machine learning to discover and predict the patterns and rules by which nature survives and distributes itself in the harshest landscapes on Earth," Warren-Rhodes said in a statement (opens in new tab). "We hope other astrobiology teams adapt our approach to mapping other habitable environments and biosignatures."

Such machine learning tools, the researchers say, could be applied to robotic planetary missions like that of NASA's Perseverance rover, which is currently hunting for traces of life on the floor of Mars' Jezero Crater.

"With these models, we can design tailor-made roadmaps and algorithms to guide rovers to places with the highest probability of harboring past or present life no matter how hidden or rare," Warren-Rhodes explained.

The team chose Salar de Pajonales as a testing stage from their machine learning model because it is a suitable analog for the dry and arid landscape of modern-day Mars. The region is a high-altitude dry salt lakebed that is blasted with a high degree of ultraviolet radiation. Despite being considered highly inhospitable to life, however, Salar de Pajonales still harbors some living things.

The team collected almost 8,000 images and over 1,000 samples from Salar de Pajonales to detect photosynthetic microbes living within the region's salt domes, rocks and alabaster crystals. The pigments that these microbes secrete represent a possible biosignature on NASA's "ladder of life detection," (opens in new tab) which is designed to guide scientists to look for life beyond Earth within the practical constraints of robotic space missions.

The team also examined Salar de Pajonales using drone imagery that is analogous to images of Martian terrain captured by the High-Resolution Imaging Experiment (HIRISE) camera aboard NASA's Mars Reconnaissance Orbiter. This data allowed them to determine that microbial life at Salar de Pajonales is not randomly distributed but rather is concentrated in biological hotspots that are strongly linked to the availability of water.

Warren-Rhodes' team then trained convolutional neural networks (CNNs) to recognize and predict large geologic features at Salar de Pajonales. Some of these features, such as patterned ground or polygonal networks, are also found on Mars. The CNN was also trained to spot and predict smaller microhabitats most likely to contain biosignatures.

For the time being, the researchers will continue to train their AI at Salar de Pajonales, next aiming to test the CNN's ability to predict the location and distribution of ancient stromatolite fossils and salt-tolerant microbiomes. This should help it to learn if the rules it uses in this search could also apply to the hunt for biosignatures in other similar natural systems.

After this, the team aims to begin mapping hot springs, frozen permafrost-covered soils and the rocks in dry valleys, hopefully teaching the AI to hone in on potential habitats in other extreme environments here on Earth before potentially exploring those of other planets.

The team's research was published this month in the journal Nature Astronomy (opens in new tab). (opens in new tab)

Follow us on Twitter @Spacedotcom (opens in new tab) and on Facebook (opens in new tab).

Original post:
Artificial intelligence could help hunt for life on Mars and other alien worlds - Space.com