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Automotive is often at forefront of technology. For decades, it has used robots to build its assembly lines. It has also been a pioneer in industrial intelligence (AI).
Semi-autonomous driving and autonomous driving are two of the biggest problems in the automotive industry.
These vehicles are equipped with a variety of sensors, cameras, and processors that can provide massive amounts of data to help them avoid obstacles, navigate in traffic, respond and signify, stop and park, and other functions required for driving.
Driverless cars need to be able to “see” and react quickly in an actual situation. Advanced AI-based decision-making and processing are required to crunch all driving-related numbers in real-time.6 Ways AI Is Driving Automotive Innovation
The automotive industry is one sector that has seen rapid growth in AI because of its size, profit margins, and fierce competition with the likes of GM and Toyota.
According to Tractica, the total AI market in automotive will be approximately $27 billion by 2025.
Also read: Top 5 Automation Tools to Streamline Workflows for Busy IT Teams1. Nauto
Nauto has developed a predictive AI system to help you avoid collisions.
It includes vision tech that is used by over 700 fleets. It can detect more than 40 risk factors within and outside a vehicle and warns of potential collisions. It was used by the Delivery Authority, a last-mile delivery fleet in the Greater Chicago area, to reduce collisions by 81%.
Yoav Banin is the chief product officer of Nauto. Nauto uses AI-native technology and data science to predict collisions and prevent them from happening, Yoav Banin said.
Nauto uses AI to understand driver behavior, rather than relying on vehicle-centric telematics or cameras to determine driving risk. To deliver audible alerts, it analyzes subtle indicators such as distraction, drowsiness, and cell phone use.
Also read: What Is The Best Time ⌛ and Day 📅 To Post On Instagram? It Is Definitely NOT ❌ Sunday (A Complete Guide)2. Tesla
Tesla is actively involved in AI on many fronts.
It just revealed the DI custom chip, which is part of Tesla’s Dojo supercomputer systems at its Tesla AI Day. This chip is manufactured in 7-nanometers and has 362 teraflops processing power.
This chip has more power than an exaflop, with 25 DI chips per tile and 120 tiles distributed across multiple cabinets. It is enough to transform the automotive AI game.
This technology is being developed in partnership with Intel, Nvidia, and Graphcore. This partnership will speed up the training of AI models to allow them to recognize key details from Tesla’s vehicle video feeds.3. Kawasaki
Kawasaki SoftBank use AI to create next-generation motorcycles. They can adapt to the rider’s needs and grow with them.
Also read: The Proven Top 10 No-Code Platforms of 20234. Jeep
Active-driving assistance has been updated to improve driver safety and performance. Sight Machine technology is being used by the company to continuously inspect the final assembly line for Jeep Grand Cherokees as well as other vehicles.
The system inspects 1,100 vehicles per hour, 15 exterior elements included, and uses enough intelligence to distinguish between 25 models and 11 colors with 99.9% accuracy. AI, manufacturing execution systems (MES), image analysis systems, and edge/cloud systems share the data from the inspection.
Sight Machine’s manufacturing productivity platform “gives everyone from the plant floor up to the C-suite a trusted and dynamically updating view” of production, said Jon Sobel (co-founder, CEO, Sight Machine).
Also read: How to choose The Perfect Domain Name5. Ford
Ford is at forefront of automotive AI research.
It is using AI to accelerate production in order to help propel the technology forward. Symbio Robotics technology is used to help robots that assemble torque converters in Michigan. This is in addition to the company’s own drive assist systems, and significant investment in autonomous vehicle technology.6. Driver Monitoring Systems (DMS).
The DMS is a set of sensors or cameras that are placed in the vehicle’s interior. They use computer vision (CV), to monitor driver behavior and issue warnings or alerts when drivers exhibit signs of distraction, drowsiness, or inattention.
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These AI unicorns are generating next-gen innovative solutions for efficiency and progress
Artificial intelligence is currently making its way towards becoming self-dependent. Global businesses have adapted to this interdisciplinary field and are creating a paradigm shift in almost all operations. Major AI companies are delivering some of the most avant-garde innovations to ensure that businesses continue to move forward. Here, we enlisted the top AI unicorns that are transforming global businesses in 2023.H2O.ai
H2O.ai is focused on solving complex business problems while accelerating the discovery of new technological ideas. The comprehensive AutoML capabilities can transform artificial intelligence to AI for gaining professional levels of accuracy, speed, as well as transparency. It serves multiple industries such as financial services, healthcare, telecom, manufacturing, insurance, and retail. It is focused on fraud detection, customer churn prediction, credit risk scopes, and so on.Haomao.AI
Haomao.AI is known as a leading artificial intelligence technology company focused on autonomous driving. The aim is to bring zero accidents, zero congestion, and upgrade the travel and logistics mode. It offers secure, intelligent, and easy-to-deploy capacity solutions as well as low-speed logistics intelligent hardware products while empowering industry customers.Harness
Harness is one of the top AI companies with a modern software delivery platform harnessing artificial intelligence for simplifying DevOps processes. DevOps functionalities include CI, CD, cloud costs, feature flags, infrastructure-as-code, and many other services to take pipelines to the next level. There is a wide range of products to help deliver code reliably and quickly to the users.HighRadius
HighRadius is one of the leading providers of AI-based order-to-cash and treasury platforms for more than 200 Fortune 1000 brands. Artificial intelligence offers integrated invoice-to-cash applications by going beyond the data-driven platform to deliver intelligence to clients. Autonomous finance and accounting help to cover cash forecasting, cash management, cash app, and many more.Highspot
Highspot helps to enhance the performance of sales teams by leveraging the benefits of artificial intelligence. It is known for empowering companies to elevate customer conversations to drive strategic growth through customer engagement with end-to-end analytics. This AI company offers services to multiple industries such as technology, manufacturing, life science, and financial services.Hive
Hive is one of the top AI companies to transform multiple industries such as automotive, manufacturing, hospitality, retail, communications, financial services, and many more. This company leverages artificial intelligence to offer video and image annotation, text and document annotation, audio annotation, 3D point cloud annotation, and data sourcing. It helps to use AI to unlock the next wave of intelligent automation.Horizon Robotics
Chinese AI chipmaker, Horizon Robotics, announced a massive US$600 million in funding in 2023, in its Series B financing round, which enabled the company set a new record for the AI chip-making industry. Back in 2023, Horizon Robotics became the world’s highest-valued AI chip unicorn. The funding was led by South Korean conglomerate SK, a semiconductor supplier named SK Hynix, and other top-tier manufacturers. Founded by Dr. Yu Kai, Former Director of Baidu, a deep learning research institute, Horizon Robotics received more than US$100 million in its A+ round led by chip giant Intel in 2023. Now with the investment from SK Hynix, the three-year-old startup acquired two of the world’s top three semiconductor companies as its major shareholders.Icertis Innovaccer
Innovaccer Inc is a leading healthcare data activation platform company focused on delivering more efficient and effective healthcare through the use of pioneering analytics and transparent, clean, and accurate data. Innvoaccer’s aim is to simplify complex data from all points of care, streamline the information, and help organizations make powerful decisions and realize strategic goals. Innovaccer landed US$150 million in a Series E round, adding nearly US$2 billion to its valuation. The startup was then valued at US$3.2 billion. Its previous round of funding put the big data startup in the leagues of health tech unicorns and increased its total funding by US$105 million.Inspur Cloud
Inspur Group is China’s leading cloud computing, big data service provider with three listed companies: Inspur Information, Inspur Software and Inspur International covering four large industry groups of the cloud data center, cloud services and big data, smart city, and smart enterprises. Inspur offers IT products and services that satisfy the information needs of more than 120 countries and regions.
Here are the top 5 ways AI and augmented reality are transforming map design
Maps are no longer merely a way to navigate from A to B because of technological breakthroughs like artificial intelligence and augmented reality, personalization, interactive design, and data visualization. In the last ten years, maps have experienced a fundamental change, transitioning from static, two-dimensional depictions of actual areas to immersive, interactive experiences that engage users on many different levels.
A study predicts that the market for augmented reality technology will increase from US$10.7 billion in 2023 to US$72.7 billion by 2024, with the development of AR technology being a major driver of innovation in map design. According to a report, the global market for AI software will increase from US$1.4 billion in 2024 to US$59.8 billion by 2025. The integration of AI and machine learning is also significantly contributing to the personalization of maps to particular user demands. In this article, we have mentioned the top 5 ways AI and augmented reality are transforming map design.
Enhancing Map Exploration
The way we engage with maps is evolving as a result of augmented reality (AR) technology. Information is superimposed over the actual surroundings to create a more immersive experience. For instance, a user can aim their smartphone at a building to view the architectural style and history using augmented reality. When maps and augmented reality (AR) technologies are combined, users can engage in interactive experiences that deliver pertinent information based on their location.
Because they make it easier for users to explore and learn more about their surroundings, augmented reality maps offer an improved user experience. Businesses can potentially profit from AR maps by offering nearby consumers location-based services and marketing.
The way maps are personalized to meet individual needs is changing as a result of AI and machine learning (ML). According to the user’s choices, personalization can include tailored routes, landmarks, and points of interest.
Map design increasingly includes interactive elements. The way that maps are presented is changing with the introduction of three-dimensional (3D) maps, animations, and visual effects. Zooming in and out is one interactive element that gives users a more immersive and interesting experience.
By giving additional context and details about the user’s surroundings, interactive design can improve the user experience. Developers have the option to design unique interactive maps for businesses and applications using mapping platforms like Mapbox.
Additionally, these maps can be created in a way that will increase customer engagement and their level of product satisfaction.
Location-Based Services in the Age of IoT
The Internet of Things (IoT) is being connected via location-based services to allow real-time tracking of autonomous vehicles and parking availability. Users may receive real-time information about their surroundings, such as traffic, accidents, and road closures, by integrating IoT sensors with maps.
Uncovering New Insights
To give users deeper insights and more dynamic experiences, data visualization techniques are increasingly being used.
Tools for data visualization, like heat maps, give users a visual representation of the data, making it simpler for them to interpret. Users may gain real-time information about their surroundings by combining maps and data visualization tools. Data visualization is being used by mapping companies like Carto to provide organizations with tools for analyzing and comprehending geographical data. Therefore, it is crucial to employ data visualization to give people accurate and pertinent information.
Concentration in the Banking Industry
So if the concentration in any market or industry is bad for consumers, does the same hold true for the banking sector? Most developed and industrialized countries have a high concentration within the financial sector, especially the banking industry. To put things in perspective,
Here are some figures from non-US markets:
In Finland, the top three banks control over 85% of the market.
In Norway, the top three banks have control of 84% of the market.
The top 3 banks in New Zealand and South Africa control 77% of the market.The Banking Industry in the United States of America
Things are very different in the financial sector in the United States. In fact, the U.S. banking sector has a relatively low level of concentration as compared to the markets above. In the U.S. the top 3 banks control only 19% of the market.
So what does this mean for the industry? Is low concentration the American market a good or bad thing? Let’s examine this in more detail.Concentration in the U.S. Banking Industry: A Good Thing
With the breakdown of the financial system in 2008 still fresh in our minds, concentration within the banking industry may not be a bad thing for American consumers. According to a study by the NBER (National Bureau of Economic Research), a high level of concentration can lead to higher levels of stability within the banking industry. In fact, countries with a concentration level of 72% or above had fewer instances of banking failure.How Can Concentration be Beneficial?
Strictly within the banking industry, there are 3 key benefits of a higher level of concentration. They are as follows:
Higher concentration levels mean more profits for the banks that dominate the industry. On the flip side, the interest rates and service fees will go up. However, banks, and by extension, depositors remain safe from economic shocks. If banks have higher franchise values, they will be less willing to take financial risks that may be damaging.
Its easier to monitor a few large banks in the industry than numerous small ones. Regulatory bodies and financial watchdogs have an easier task keeping an eye on dominant banks. Larger banks will also have similar operations and systems, adding to the uniformity.So What Does This Mean for America?
As we mentioned above, the U.S. has a relatively low concentration level within the banking industry. This is one of the reasons the United States sees more economic fluctuation than more concentrated markets, according to experts. According to the NBER, banking failure and bank size have a negative correlation. As the banks’ sizes increase, the number of banking failures comes down. But that’s not to say there aren’t any negative effects.How Can Concentration be Negative?
Higher levels of concentration within any industry usually lead to lower levels of competition. Prices and fees will also rise with increasingly concentrated industries. This is part of the reason we see higher HughesNet bundle prices every year. And banking is no exception. In the banking industry, more concentration means higher interest rates. Investors start balking at risky investments. There’s also the fact that less profitable niches may be ignored by more popular banks in favor of the more profitable ones. You can see this in many industries, like the aviation industry which only flies popular routes.
What is BudBlockz?
BudBlockz is an Ethereum-compatible e-commerce platform connecting entrepreneurs and corporations in the cannabis industry with the international market. It enables entrepreneurs and firms to grow at scale by bridging the gap between them and the global international market by leveraging the blockchain-based ecosystem. Its plans for the weed industry include setting up cannabis farms and dispensaries that will serve as facilities to boost product development, education, research, and accessibility to cannabis-based products and the industry, which until now has been heavily regulated. Over time, it will be helping marijuana corporations and individual entrepreneurs solve industry-wide challenges like problems in fundraising, data management, supply chain execution, logistical issues, and seed-to-sale tracking.
To facilitate all kinds of transactional uses on its platform, BudBlockz has rolled out a native token called $BLUNT, which will be used for staking, liquidity management, signing you for petitions, voting, and accessing rewards, among other purposes. Built on the ERC-20 protocol of the Ethereum network, the $BLUNT Token is available on presale.
The BudBlockz platform features a robust security framework that shields investors’ privacy and interests. The platform’s NFT marketplace is built to ensure absolute transparency, privacy, and permissionless participation. Third parties like Solidity Finance have audited its smart contracts. A unique factor about the $BLUNT Token is its deflationary nature. The token is burned at regular intervals to keep its supply limited. A scarce supply helps keep the token’s value stable by keeping price fluctuations at bay. For this purpose, BudBlockz organizes ‘How High’ Token burn events to ensure that these tokens are always in short supply.How can BudBlockz Help its Users Boost their Revenue?
One of the key reasons behind BudBlockz’s rising popularity is how it offers its users multiple opportunities to earn passive income. With more than one way to boost their revenues, users are incentivized to stay associated with the platform and contribute to its growth. Thus, BudBlockz does a fairly good job if you are looking for a cryptocurrency with high growth potential. As the platform has a policy of incentivizing participation, it doles out regular rewards in terms of $BLUNT Tokens for its users. The platform also has a limited-series NFT collection called ‘Ganja Guruz.’ These NFTs are built on the ERC-721 protocol of the Ethereum network.
The Ganja Guruz series has 10,000 units and comprises a vibrant collection of digital art pieces inspired by the video games of the 1990s. What’s more, these NFTs open an entire world of benefits to owners, including access to BudBlockz’s cannabis farms and dispensaries. Further, by submitting a KYC application, NFT buyers can also become fractional owners of these facilities. Fractional ownership of a BudBlockz cannabis farm or dispensary works like a regular asset wherein users enjoy annual dividends on their investments.
Another income-earning opportunity the BudBlockz team offers is the play-to-earn gaming ecosystem based on the cannabis industry. Players will be eligible for rewards in terms of $BLUNT Tokens if they win. These arcade games will also include regular competitions where winners can bag ETH, $BLUNT Tokens, and NFTs as prizes.Purchase or learn more about BudBlockz (BLUNT) at the links below:
I started my career as a management consultant. Excel was our temple for analytics. For a recent graduate, macros and connected models perform miracles but albeit at great effort. However, our reach was extremely limited compared to the possibilities of today. We could not process anything with images, text, audio or video easily as non-technical users. Fast forward to today, and citizen data scientists are unleashing machine learning on all of companies data to run diagnostic, predictive, and prescriptive analytics.
In that regard, in this article, we plan to explain how exactly AI is transforming how analytics is done.How is AI contributing to analytics capabilities?
More efficient thanks to automation,
More accessible thanks to improved UI, with Natural Language Processing enabling analytics tools to understand natural language queries,
And more powerful, since previously-difficult-to-analyze data, such as text and videos, are now easily analyzable.Analytics is getting automated
Analytics is time consuming and analytics talent is expensive. The war for AI talent is well documented and impacts the cost and availability of analytics talent. Data scientist who compromise a significant share of modern analytics work force are also part of the AI workforce.
Therefore automating analytics tasks has significant potential value for firms.Analysis (i.e. discovering insights and acting on them) is getting automated
AI systems are able to analyze data autonomously. Based on the results of analysis, they can take automated actions or highlight insights to employees who can decide the best course of action.
Setting up auto notifications based on simple rules has been possible since the early days of computing. Now companies can set up complex, machine learning based triggers to identify insights or automate actions. For example, a machine learning system that detects intruders to physical secure locations based on video feeds can take actions (e.g. by informing the intruder) or highlight the event to the human personnel.Report preparation is getting automated, making analytics more accessible
Analytics are only as useful as long as it is accessible. Natural language generation (NLG) enables automated report preparation.Analytics is becoming more accessible
We mentioned the increasing cost of analytics talent due to the increased demand for data science talent. Data scientists are expensive as they are PhDs or graduates of computer science and other quantitative fields who have an understanding of statistics and computation. This enables them to build complex models including machine learning models which can extract insights from data.
What if you did not need data scientists to extract insights from data? Users can use natural language to easily and intuitively find answers. This is supported through natural language (NL) query (also called natural language interaction – NLI or natural language user interaction – NLUI). For example, Thoughtspot, which enables companies to run complex analytics queries via natural language, raised ~250m on a ~2 billion valuation in 2023.Scope of analytics is increasing thanks to AI Unstructured data is becoming analyzable thanks to AI
Excel and other legacy analytics tools are not effective at dealing with unstructured data such as text, audio and images. Advances in AI greatly expand the scope of analytics when compared to the days when excel was the primary analytics tool. Some ways that AI is becoming integrated in analytics includes these areas:
Natural language processing (NLP) enables analysis of text
Transcription enables speech analytics
Computer vision enables image and video analyticsSemi-structured data is becoming analyzable thanks to AI
A significant share of company data is locked up in semi-structured documents such as invoices, receipts, order forms etc. Deep learning based data extraction solutions enable companies to extract entities from their semi-structured data and use them to understand their business in more detail.New techniques enable analysis of anonymized personally identifiable data, expanding scope of analytics
Anonymization via synthetic data is a rather old technology. However with the increasing demand for analytics and increasing protection of personal data, demand for anonymized data has increased. Numerous synthetic data vendors are enabling companies to create synthetic (machine-generated, anonymized but following the same distributions as the underlying personally identifiable data) copies of their customers so they can run detailed simulations and improve their offering.Analysis is becoming more powerful thanks to AI
While simple regressions guided business decision making for hundreds of years, businesses now rely on machine learning. Machine learning is the use of statistical techniques to enable computers to identify and learn the patterns in the given data, rather than being programmed explicitly for a certain function.
Some analytics techniques that can be enhanced with AI and machine learning include:
Prediction: Using short and long term variability in data to enhance forecasting efforts.
Pattern recognition: Understanding normal trends in order to spot anomalies, as is often the case in fraud detection.
Classification algorithms: Grouping and organizing of data, includes clustering.Which industries rely on AI in analytics?
All industries can benefit from AI-powered analytics. We had a deep-dive into:
Some tools that can make the analytics process easier include:
NameStatusNumber of Employees Apache PredictionIOPrivate1,001-5,000 AyasdiPrivate51-200 Einstein Analytics by SalesforcePublic10,000+ IBM CognosPublic10,000+ Infosys niaPublic10,001+ IperceptionsPrivate51-100 Microsoft Power BIPublic10,000+ MicroStrategy IncPublic1,001-5,000 Oracle Analytics ClousPublic10,000+ PurePredictivePrivate21-50 Qlik SensePrivate1,001-5,000 SAP Analytics CloudPublic10,000+ Sisense IncPrivate501-1,000 StatisticaPrivate10,001+ TableauPublic1,001-5,000 TelliusPrivate11-50 ThoughtSpotPrivate201-500 TIBCO Software IncPrivate1,001-5,000For more on analytics
If you are interested in learning more on analytics, read:
Finally, if you believe your business would benefit from leveraging an analytics solution, we have data-driven lists of vendors on our analytics hub.
We will help you choose the best one suited to your needs:
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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