Trending March 2024 # Top 10 Ai Chip Makers Of 2023: In # Suggested April 2024 # Top 10 Popular

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As the figure above illustrates, the number of parameters (consequently the width and depth) of the neural networks increase, which indicates greater model size. To derive meaningful results from existing deep learning models, organizations require increased computing power and memory bandwidth. 

Powerful general-purpose chips (such as CPUs) cannot support such sophisticated deep learning models. Therefore, AI chips that enable parallel computing capabilities are increasingly in demand, and according to McKinsey, this trend will continue.

However, even Intel, which has numerous world-class engineers and a strong research background, needed three years of work to develop its own AI chip. Therefore, for most companies, buying chips or platforms that run on a purpose built AI chip, from these vendors is the only way to develop powerful deep learning models. This article will introduce 10 AI chip vendors to help companies choose the right one.

Who are the leading AI chip producers?

1. Nvidia

Source: Google

Nvidia has been producing graphics processing units (GPUs) for the gaming sector since 1990s. The PlayStation 3 and Xbox both use Nvidia graphics arrays. The company also makes AI chips such as Volta, Xavier, and Tesla. Thanks to the generative AI boom, NVIDIA had excellent results in Q2 2023 and reached a trillion in valuation.

NVIDIA’s chipsets are designed to solve business problems in various industries. Xavier, for example, is the basis for an autonomous driving solution, while Volta is aimed at data centers. DGX™ A100 is the flagship AI chip of Nvidia which is also designed for data centers. Product integrates 8 GPUs and up to 640GB GPU memory. Nvidia Grace is the new AI chip model that the company released for the HPC market in 2023.

2. Intel

Intel is one of the largest players in the market and has a long history of technology development. In 2023, Intel became the first AI chip company in the world to break the $1 billion sales barrier. 

Intel’s Xeon processors, which are appropriate for a variety of jobs, including processing in data centers, have had an impact on the company’s commercial success. In comparison to earlier generations, the third generation Xeon platinum series has up to 40 cores and 1.6 times greater memory bandwidth, and 2.66 times greater memory capacity compared to previous generation.

Gaudi is the neural network training accelerator  from Intel. This product is able to scale as models get larger and has a relatively low total cost of ownership. For inferencing, Intel has the Goya, optimized for throughput and latency. 

Intel® NCS2 is the latest AI chip from Intel and was developed specifically for deep learning. 

3. Google Alphabet

Google Cloud TPU is the purpose-built machine learning accelerator chip that powers Google products like Translate, Photos, Search, Assistant, and Gmail. It can be used via the Google Cloud implementation. Edge TPU, another accelerator chip from Google Alphabet, is smaller than a one-cent coin and is designed for edge devices such as smartphones, tablets, and IoT devices.

4. Advanced Micro Devices (AMD)

AMD is a chip manufacturer that has CPU, GPU and AI accelerator products. For instance, AMD’s Alveo U50 data center accelerator card has 50 billion transistors. Accelerator can run 10 million embedding datasets and perform graph algorithms in milliseconds.

IBM launched its “neuromorphic chip” TrueNorth AI in 2014. TrueNorth contains 5.4 billion transistors, 1 million neurons, and 256 million synapses, so it can efficiently perform deep network inference and deliver high-quality data interpretation.

In April 2023, IBM launched its new hardware, “IBM Telum Processor”. Its development process took three years and is aimed to improve efficiency of use of large datasets.  According to IBM, it is suitable to use Telum processors for missions such as preventing fraud immediately due to its improved processor core and memory compared to previous AI chips of the company.

The below video further introduces Telum Processor:

Who are the leading AI chip startups?

We would also like to introduce some startups in the AI chip industry whose names we may hear more often in the near future. Even though these companies were founded only recently, they have already raised millions of dollars.

Source: Statista and Reuters

6. SambaNova Systems

SambaNova Systems was founded in 2023 with the goal of developing high-performance, high-precision hardware-software systems. The company has developed the SN10 processor chip and raised more than $1.1 billion in funding.

It is important to note that, rather than selling its AI processors, SambaNova Systems builds data centers and leases them to the businesses. Product as service approach of SabaNova Systems might enhance product stewardship and force them to produce more durable products since they own it through the lifecycle. AIMultiple evaluates product as service approach as one of the circular economy best practices. 

7. Cerebras Systems

Cerebras Systems was founded in 2024. In April 2023, the company announced its new AI chip model, Cerebras WSE-2, which has 850,000 cores and 2.6 trillion transistors. Undoubtedly, the WSE-2 is a big improvement over the WSE-1, which has 1.2 trillion transistors and 400,000 processing cores. 

Celebra’s system works with many pharmaceutical companies such as AstraZeneca and GlaxoSmithKline because the effective technology of WSE-1 accelerates genetic and genomic research and shortens the time for drug discovery.

8. Graphcore

Graphcore is a British company founded in 2024. The company announced its flagship AI chip as IPU-POD256. Graphcore has already been funded with around $700 million.

Company has strategic partnerships between data storage corporations like DDN, Pure Storage and Vast Data. Graphcore works with research institutes around the globe like Oxford-Man Institute of Quantitative Finance, University of Bristol and Berkeley University of California are other reputable research organizations that use Graphcore’s AI chips.

9. Groq

Groq has been founded by former Google employees. The company represents a new model for AI chip architecture that aims to make it easier for companies to adopt their systems. The startup has already raised around $350 million and produced its first models such as GroqChip™ Processor, GroqCard™ Accelerator, etc.

On the first of March, Groq acquired Maxeler, which has high performance computing (HPC) solutions for financial services applications.

10. Mythic

Mythic was founded in 2012. It developed products such as M1076 AMP, MM1076 key card, etc., and has already raised about $150 million in funding.

You can also check our sortable list of companies working on AI chips.

You might enjoy reading our articles on TinyML and accelerated computing.

If you have questions about how AI hardware can help your business, we can help:

This article was drafted by former AIMultiple industry analyst Görkem Gençer.

References

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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Top 13 Use Cases / Applications Of Ai In Manufacturing In 2023

The industrial manufacturing industry is the top adopter of artificial intelligence, with 93 percent of leaders stating their organizations are at least moderately using AI.

Manufacturers are frequently facing different challenges such as unexpected machinery failure or defective product delivery. Leveraging AI and machine learning, manufacturers can improve operational efficiency, launch new products, customize product designs, and plan future financial actions to progress on their digital transformation.

Why is AI important in the manufacturing industry?

Implementing AI in manufacturing facilities is getting popular among manufacturers. According to Capgemini’s research, more than half of the European manufacturers (51%) are implementing AI solutions, with Japan (30%) and the US (28%) following in second and third.

The same study also reveals that the most popular AI use cases in manufacturing are improving:

maintenance (29% of manufacturing AI use cases)

quality (27%)

This popularity is driven by the fact that manufacturing data is a good fit for AI/machine learning. Manufacturing is full of analytical data which is easier for machines to analyze. Hundreds of variables impact the production process and while these are very hard to analyze for humans, machine learning models can easily predict the impact of individual variables in such complex situations. In other industries involving language or emotions, machines are still operating at below human capabilities, slowing down their adoption.

The COVID-19 pandemic also increased the interest of manufacturers in AI applications. As seen on Google Trends graph below, the panic due to lockdowns may have forced manufacturers to shift their focus to artificial intelligence.

What are the common AI use cases in manufacturing? 1. Predictive maintenance

Manufacturers leverage AI technology to identify potential downtime and accidents by analyzing sensor data. AI systems help manufacturers forecast when or if functional equipment will fail so its maintenance and repair can be scheduled before the failure occurs. Thanks to AI-powered predictive maintenance, manufacturers can improve efficiency while reducing the cost of machine failure.

2. Generative design

Generative design uses machine learning algorithms to mimic an engineer’s approach to design. Designers or engineers enter parameters of design (such as materials, size, weight, strength, manufacturing methods, and cost constraints) into generative design software and the software provides all the possible outcomes that can be created with those parameters. With this method, manufacturers quickly generate thousands of design options for one product.

3. Price forecasting of raw material

The extreme price volatility of raw materials has always been a challenge for manufacturers. Businesses have to adapt to the unstable price of raw materials to remain competitive in the market. AI-powered software like can predict materials prices more accurately than humans and it learns from its mistakes.

4. Robotics

Industrial robots, also referred to as manufacturing robots, automate repetitive tasks, prevent or reduce human error to a negligible rate, and shift human workers’ focus to more productive areas of the operation. Applications of robots in plants vary. Applications include assembly, welding, painting, product inspection, picking and placing, die casting, drilling, glass making, and grinding.

Industrial robots have been in manufacturing plants since the late 1970s. With the addition of artificial intelligence, an industrial robot can monitor its own accuracy and performance, and train itself to get better. Some manufacturing robots are equipped with machine vision that helps the robot achieve precise mobility in complex and random environments.

Cobots are another robotics application that uses machine vision to work safely alongside human workers to complete a task that cannot be fully automated. Feel free to learn more about cobots with our comprehensive guide.

5. Edge analytics

Edge analytics provides fast and decentralized insights from data sets collected from sensors on machines. Manufacturers collect and analyze data on edge to reduce time to insight. Edge analytics has three use cases in manufacturing:

Improving production quality and yield

Detecting early signs of deteriorating performance and risk of failure

Tracking worker health and safety by using wearables

To learn more about analytics in manufacturing, feel free to read our in-depth article about the top 10 manufacturing analytics use cases.

6. Quality assurance

Quality assurance is the maintenance of a desired level of quality in a service or product. Assembly lines are data-driven, interconnected, and autonomous networks. These assembly lines work based on a set of parameters and algorithms that provide guidelines to produce the best possible end-products. AI systems can detect the differences from the usual outputs by using machine vision technology since most defects are visible. When an end-product is of lower quality than expected, AI systems trigger an alert to users so that they can react to make adjustments.

You can also check the lists of data annotation and AI/ML tools and services to find the option that best suits your project needs:

7. Inventory management

Machine learning solutions can promote inventory planning activities as they are good at dealing with demand forecasting and supply planning.  AI-powered demand forecasting tools provide more accurate results than traditional demand forecasting methods (ARIMA, exponential smoothing, etc) engineers use in manufacturing facilities. These tools enable businesses to manage inventory levels better so that cash-in-stock and out-of-stock scenarios are less likely to happen.

8. Process optimization

AI-powered software can help organizations optimize processes to achieve sustainable production levels. Manufacturers can prefer AI-powered process mining tools to identify and eliminate bottlenecks in the organization’s processes. For instance, timely and accurate delivery to a customer is the ultimate goal in the manufacturing industry. However, if the company has several factories in different regions, building a consistent delivery system is difficult.

By using a process mining tool, manufacturers can compare the performance of different regions down to individual process steps, including duration, cost, and the person performing the step. These insights help streamline processes and identify bottlenecks so that manufacturers can take action.

For example, a manufacturer that employed a process mining tool in their procure-to-pay processes decreased deviations and maverick buying worth to $60,000.

9. AI-Powered digital twin use cases

A digital twin is a virtual representation of a real-world product or asset. By combining AI techniques with digital twins, manufacturers can improve their understanding of the product and allow businesses to experiment in future actions that may enhance asset performance. There are typically 4 applications of digital twins in manufacturing:

10. Product development

Manufacturers can use digital twins before a product’s physical counterpart is manufactured. This application enables businesses to collect data from the virtual twin and improve the original product based on data.

11. Design customization

Due to the shift toward personalization in consumer demand, manufacturers can leverage digital twins to design various permutations of the product. This allows customers to purchase the product based on performance metrics rather than its design.

12. Shop floor performance improvement

A digital twin can be used to monitor and analyze the production process to identify where quality issues may occur or where the performance of the product is lower than intended.

13. Logistics optimization

Digital twins allow manufacturers to gain a clear view of the materials used and provide the opportunity to automate the replenishment process.

What are the benefits of AI in manufacturing? Safety

Manufacturing is one of the highest-risk industrial sectors to be working in with more than 3,000 major injuries and nine fatalities occurring each year. The involvement of robots in high-risk jobs can help manufacturers reduce unwanted accidents.

Cost Reduction

AI technologies can reduce the operation costs of manufacturers due to several applications:

Leveraging AI technologies can enhance organizations’ analytics capability so that they can use their resources more efficiently, make better forecasts, and reduce inventory costs. Thanks to better analytics capabilities, companies can also switch to predictive maintenance leading to eliminating downtime costs and reducing maintenance costs.

This one is obvious but manufacturers don’t need to pay monthly salaries to robots. However, robots require CAPEX which needs to be weighed against the recurring cost of labor.

Faster decision making

Thanks to IoT sensors, manufacturers can collect large volumes of data and switch to real-time analytics. This allows manufacturers to reach insights sooner so that they can make operational, real-time data-driven decisions.

24/7 production in dark factories

Factories without any human labor are called dark factories since light may not be necessary for robots to function. This is a relatively new concept with only a few experimental 100% dark factories currently operating. However, dark factories will increase over time with the application of AI and other automation technologies since they have the potential to unleash significant savings, end workplace accidents and expand their production capacity.

Read more on AI applications in different industries:

If you still have questions on how AI revolutionizing the manufacturing industry, don’t hesitate to contact us:

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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Watch Out For The Top 10 Ai Startups Leading The Ai Race In 2023

These top 10 AI startups are revolutionizing the technology market in 2023

There are multiple AI Startups that through their innovation and improving technology are solving problems at a pace never seen before. And there seem to be no breaks to these developments in the AI industry by these startups. From providing healthcare services to presenting highly intelligent AI and ML solutions to almost every sector, AI startups are the hottest brands in the market. Here is the list of top 10 AI startups leading the AI race in 2023.

UBTECH Robotics

Chinese AI and humanoid robotic company Ubtech Robotics recently introduced Ubtech’s educational robot Alpha Mini into local kindergartens to help further artificial intelligence education for preschool children. This project is targeted toward children aged 3-5 and will cover 300 nursery centers in Seoul. UBTECH Robotics, a developer of intelligent humanoid robotics and AI technologies, has unveiled Walker X, the latest version of its groundbreaking bipedal humanoid robot. It is another step closer to becoming the gold standard in humanoid robotics.

UISEE Technology

Autonomous driving technology company UISEE and the Guangdong Airport Authority have jointly unveiled a “Smart Logistics Laboratory” and the launch of an unmanned tractor project. This project introduced five unmanned tractor models from UISEE, designed to replace manual transportation on the outbound business lines of domestic cargo terminals, as well as the inbound and outbound business lines of international cargo terminals. Teksbotics & UISEE jointly pilot new package delivery solutions using a self-driving vehicle to design and build a cost-effective autonomous delivery vehicle for the last mile delivery.

UiPath

UiPath fourth-quarter results topped estimates, guidance for 2023 was disappointing, and the stock finished March down 38%. It has provided four reasons for the guidance: Russia, FX headwinds, general macro concerns, and a sales leadership transition. UiPath and DocuSign, both companies will generate significantly slower growth this year and also face near-term margin pressure.

Uniphore

Uniphore Technologies Inc, a conversational automation platform, on Wednesday said it has acquired an artificial intelligence (AI)-powered knowledge automation solution company Colabo. To help IVAs and live agents deliver better customer interactions. This acquisition brings together unique capabilities to arm enterprises with new tools that provide a quick resolution to consumer queries and empower agents with real-time, actionable information

Uptake

Uptake is the industrial analytics platform that delivers products to major industries to increase productivity, security, safety, and reliability. Uptake is a World Economic Forum Technology Pioneer and three-time CNBC Disruptor 50 honoree. It helps industrial companies digitally transform with open, purpose-built software that delivers outcomes that matter and industrial companies are once again the creators of economic growth and opportunity.

VAST Data

Vast Data combined its all-flash, high-performance storage with Vertica’s Eon Mode Architecture to give data warehouse-like responses to data lakes in a converged product. Nvidia had increased its investment in storage startup Vast Data, it was not exactly clear how or if the companies already were working together.  The companies announced a few weeks ago during Nvidia’s GTC Spring event that Nvidia Bluefield DPUs are powering Israel-based Vast Data’s new Ceres storage platform.

Vectra

Vectra AI, a leader in AI-driven threat detection and response for hybrid and multi-cloud enterprises, today released the findings of its latest Security Leaders Research Report. This alarming statistic comes as cyber threats increase and security and IT teams face mounting expectations to keep their organizations protected from such threats. Now AttackIQ and Vectra joined together to help customers enable a proactive, threat-informed defense.

VerbIT

VerbIT is a Tel Aviv- and New York-based AI transcription & real-time captioning solution which provides the highest accuracy & fastest transcription models that have acquired Take Note in the UK. This acquisition marks VerbIT’s entrance into the market research space and increased presence in Europe. VerbIT has recently started paying its transcribers and reviewers even less money than before. The rate for editing was 30 cents per audio minute, it is currently 24 cents or even less on occasions for a standard job.

WeRide YITU Technology

Chinese artificial intelligence company, Yitu Technology is considering an initial public offering in Hong Kong after tightening regulatory scrutiny stalled an earlier attempt to list in Shanghai. Yitu, whose application for a Starboard IPO was withdrawn last month, could file for a listing as soon as later this year. YITU has obtained a number of authoritative international standards certifications for information security and privacy protection, including ISO/IEC 27701:2024 and ISO/IEC 27001:2013. ISO/IEC 27701:2024, which is the world’s strictest.

Top 10 Best Indian Ai Stocks To Buy For Good Growth In 2023

These AI stocks of India will lead in 2023 and You must be aware of them

With the changing business landscape, AI is surely playing an important role in every industry vertical. It can mimic human interaction and show human-like capabilities. Today top listed

Tata Elxsi

The Bosch Center for Artificial Intelligence (BCAI) was founded in 2023 to apply cutting-edge AI technology throughout, Bosch products and services, resulting in innovative solutions. Bosch created the technological groundwork for AI to have a real-world impact. Its research produces differentiation in six areas using data from all of Bosch’s disciplines, with a focus on core

Kellton Tech

Kellton Tech Solutions is a Hyderabad-based information technology and outsourcing company with locations in the United States and Europe. With over 1400 people, the company generated net revenues of Rs. 7.39 billion. Kellton Tech creates cutting-edge, focused AI solutions to challenges that traditionally needed a great lot of human intellect, ranging from machine learning to

Happiest Minds

Happiest Minds To help organizations provide immersive consumer experiences and surpass the competition, combine

Zensar Technologies

Zensar Technologies is betting on Artificial Intelligence (AI). The company’s go-to-market strategy is now pivoting away from digital and toward disruptive AI. Zensar AIRLabs, the company’s R&D department, has filed for 100 patents in the last several years and is now entirely focused on AI. Zensar announced the initial set of platforms for seven major areas last week, including sales, marketing, IT, talent supply chain, HR, collaboration, projects, and programs, to help customers create value. Over a three-year period, Nifty IT Stock returned 15.63 percent, compared to Nifty IT, which returned 106.55 percent to investors. Cyientassists companies in achieving business objectives rather than merely new tools and technologies. For self-driving cars, AI can detect changes in the real environment and update maps in real-time. Such navigation helpers can aid autonomous vehicles in fully comprehending the world around them and taking the appropriate actions to avoid collisions. Rather than simply providing new tools and technologies, it aids businesses in attaining their goals. Over a three-year period, the stock returned 24.4 percent, while the Nifty IT returned 106.55 percent to investors.  

Persistent Systems

Persistent Systems Persistent turns AI and machine learning dreams into reality with solutions that aid at every stage of AI and machine learning development. With a methodology that helps prioritize use cases, design platform architecture, scale model development, and operationalize models across the company, our solutions ensure that you realize successful outcomes from your AI and ML investments. Annual sales growth of 16.16 percent surpassed the company’s three-year CAGR of 10.75 percent. Over a three-year period, the stock returned 208.41%, compared to 106.55 percent for the Nifty IT.  

Saksoft

Saksoft assists customers in achieving transformational transformations through intelligent decisions, increased efficiency and productivity, enhanced customer experience, and service innovation by leveraging the critical combination of Artificial Intelligence and automation. Saksoft accelerates digital transformation and applies intelligent automation to solve business problems by combining automation and modern technologies such as RPA, machine learning, IoT, and artificial intelligence. Over a three-year period, the Saksoft shares returned 118.06 percent, while the Nifty IT provided investors a 106.55 percent return.  

Oracle Financials

Oracle Financials Oracle can assist you in implementing AI in your company and IT processes. With Oracle Cloud applications and platform, as well as Oracle Autonomous Database, all operating on Oracle’s Gen 2 Cloud, you can speed up automation, minimize human errors, and gain greater business insights. Only 2.35 percent of trading sessions in the last 16 years had intraday gains of more than 5%. The stock returned -11.82 percent over three years, compared to 44.16 percent for the Nifty 100.  

Affle

Top 6 Use Cases Of Generative Ai In Education

Due to the COVID-19 pandemic, the use of digital technologies to enhance education has significantly increased as many students around the world have had to shift to online learning. For example, investment in education for adopting innovative technologies increased from $7 billion to $20 billion during the pandemic as trends suggest. However, digital technologies also have the potential to transform the education experience in other ways beyond just online classes. The application of generative AI in education is an example to this.

Generative AI is a digital technology that can quickly create new and realistic visual, textual, and animated content. In other articles, we investigated its use cases in different  sectors, such as healthcare and banking. While other technologies like conversational AI and robotic process automation (RPA) are implemented in education, generative AI is not properly implemented in education. Despite this, it has potential use cases for improving it. This article explains the top 6 potential ways to use generative AI in education.

1. Personalized Lessons

Personalized lesson plans are a powerful way to ensure that students receive the most effective education tailored specifically to their needs and interests. These lesson plans can be generated by using AI-powered algorithms to analyze student data, such as:

Their past performance

Their skills 

And any feedback they might have given regarding content

AI-based systems can leverage such information to generate customized curriculum that is more likely to engage each student and help them reach their potential. This can be important for children with learning disabilities or disorders.

For example, Speechify is a generative AI-driven tool. It offers text-to-speech or speech-to-text generations on desktops or on online use.

2. Course Design

Generative AI tools can help design and organize course materials, including syllabi, lesson plans, and assessments. They can also personalize course material based on students’ knowledge gaps, skills and learning styles, such as practice problems or interactive exercises. 

Generative AI can create simulations and virtual environments once paired with other technologies, such as virtual reality. Consequently, it offers more engagement and interactive courses, improving students’ learning experience.   

For example, a generative AI system could create a virtual laboratory setting where students can conduct experiments, observe the results, and make predictions based on their observations.

3. Content Creation for Courses 

Generative AI can assist in creating new teaching materials, such as questions for quizzes and exercises or explanations and summaries of concepts. This can be especially useful for teachers who need to create a large amount and a variety of content for their classes. By using AI, it is possible to create modified or brand-new content from the original content.

Furthermore, generative AI can facilitate generating additional materials to supplement the main course materials, such as: 

Reading lists

Study guides 

Discussion questions 

Flashcards

Summaries. 

Also, AI can generate scripts for video lectures or podcasts, streamlining multimedia content creation for online courses. Image generation is another important ability of generative AI for education. Teachers may want to generate images with specific modifications that respond to particular course needs.

For example, NOLEJ offers an e-learning capsule that is AI generated in only 3 minutes. This capsule provides an interactive video, glossary, practice, and summary for a target topic (see Figure 1 below).

More established companies are using AI to generate content that supports their main products. For instance, Duolingo, a language learning platform, uses GPT-3 to correct French grammar and create items for their English test. The company concludes that with the implementation of GPT-3, second language writing skills of customers are increased.

4. Data Privacy Protection for Analytical Models

Using synthetic data, which is created by AI models that have learned from real-world data, can provide anonymity and protect students’ personal information. Synthetic data sets produced by generative models are effective and useful for training other algorithms, while being secure and safe to use.

For more on how generated synthetic data enables data privacy, you can check out these articles:

5. Restoring Old Learning Materials

Generative AI can improve the quality of outdated or low-quality learning materials, such as historical documents, photographs, and films. By using AI to enhance the resolution of these materials, they can be brought up to modern standards and be more engaging for students who are used to high-quality media.

These updates can also make it easier for students to read, analyze, and understand the materials, leading to a deeper understanding of the content and, ultimately, better learning outcomes.

Using a version of generative AI, Generative Adversarial Networks (GANs), it is possible to restore low-quality images and remove simple watermarks. In Figure 2 below, you can see a prototype for image restoration via GANs. Such image restoration can be adapted to educational materials. For example, in art and design schools, restoring old images would provide the detection of important details of artworks. Also in history classes and research, scanning and restoring old documents can be facilitated.

Figure 2. Image restoration with GANs. (Source: Towards Data Science)

6. Tutoring 

Another use case of generative AI is to provide tutoring. Generative AI can be used to create virtual tutoring environments, where students can interact with a virtual tutor and receive real-time feedback and support. This can be especially helpful for students who may not have access to in-person tutoring.

According to academic studies, private tutoring children with severe reading difficulty improved their reading skills by 50% in a year. However, providing tutoring to all students can be a challenge. Generative AI can tackle this issue by creating virtual tutoring environments. In these environments, students can interact with a virtual tutor and receive feedback and support in real-time. This can be especially helpful for students who may not have access to in-person tutoring.

For example, TutorAI is trying to implement this kind of use of generative AI in education. It offers an educational platform that generates interactive content on a variety of topics.

Another generative AI work for teaching purposes can be the implementation of chatbots for tutoring. Chatbot Life’s 2023 chatbot report shows that education is the third biggest industry benefiting from chatbots.

Lately, Chat GPT from OpenAI stormed the internet with its ability to engage in highly personalized conversations and definitive answers. It can answer course-related questions from a variety of domains, and can even write essays on the target topic. 

On the other hand, implementing generative AI-based chatbots specified and regulated for educational purposes is a future plan. However, it offers potential uses and benefits:

One potential use would be to provide around-the-clock support to students and their parents, including help with homework.

Generative chatbots can also assist with administrative tasks, such as answering student or parent questions, freeing up time for educators to focus on other tasks, such as grading and lesson planning.

The flexibility and natural feeling of generative chatbots make them useful in educational settings, particularly with elementary and middle school children.

Challenges of generative AI in education

Although generative AI has a lot of potential to improve educational practices, it may also pose some potential challenges. These can be shortly listed as:

Biases in educational materials

False or inaccurate information

Abuse of it for self interest

Unemployment risks for some teachers or other education professionals

For a detailed discussion on the ethical challenges of generative AI, you can check our article.

For more on generative AI

To explore more about generative AI, you can check our other articles:

Discover the top generative AI tools from our detailed list sorted by category:

If you have questions regarding generative AI, feel free to reach out:

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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Top 10 Ai Tools For Academic Research

Following is a list of the top 10 AI tools for academic research helpful for researchers

Scholars and Students have devoted countless hours to academic research and writing throughout history. Even though researchers now have access to more information and AI tools than ever before thanks to technology and the internet, it can be challenging to find the right AI tools for research.

Scholars and researchers require assistance sorting through and organizing sources due to the abundance of information available today. In addition, scholars and researchers must write informative, engaging, and well-written articles and reports due to the ongoing pressure to publish. Here is the list of AI tools for academic research.

1. Consensus: 2. ChatPDF:

Users can converse with a PDF document through the AI application ChatPDF. Without signing in, users can interact with any PDF they own, such as books, research papers, manuals, articles, and legal documents. To comprehend the content of PDF files and provide pertinent responses, ChatPDF makes use of a next-generation AI model comparable to ChatGPT.

3. Scite:

Scite’s Assistant, an AI-powered research tool, lets users work together on essays and research papers, find evidence to back up their claims and find evidence to refute them. Clients might enter straightforward questions to get reactions in light of the total texts of exploration distributions. The application can be used by users to find reliable information, search through millions of research articles, and create grant proposals or essay drafts.

4. Elicit:

Elicit, a machine learning tool, is used by the AI research assistant to help automate research procedures. Without specific keyword matches, it can locate relevant articles and extract important information. Inspire may likewise give different exploration exercises, including conceptualizing, summing up, and text-order, as well as summing up central issues from the report that are pertinent to the client’s request.

5. Trinka:

Online sentence structure checker and language proofreader Trinka artificial intelligence was made for specialized and scholastic composition. It is made to catch errors that other grammar checkers miss, like issues with subject-verb agreement, syntax, word choices, the use of pronouns and articles, and technical spelling. In addition, it incorporates a professional tone, the use of technical words, conciseness that goes beyond grammar and spelling, and style guides.

6. Scholarcy:

The online summarizing tool Scholarcy is a simple way to quickly examine and evaluate the significance of documents like articles, reports, and book chapters. Any Word or PDF document can be used to create summary flashcards that are displayed in an organized and easy-to-understand manner.

7. Academic Semantics:

The vast majority know about Google Researcher, which uses Google’s web search tool ability to list academic distributions. But if you’re doing any kind of scientific research, you should try Semantic Scholar. This AI-powered search and discovery tool, made available by publisher partnerships, data suppliers, and web crawls, enables you to keep up with more than 200 million academic publications.

8. Bit.ai:

Utilizing the internet to find information is a blessing. The amount of data that is accessible and the fact that it can be found in a variety of formats, such as blogs, essays, films, infographics, and images, present two challenges. Finding and organizing all of the data related to your study’s many areas might take a lot of effort.

9. SciSpace:

SciSpace is a platform driven by AI that lets people read, understand, and submit scientific articles. Its extensive searchable database contains more than 270 million articles, authors, subjects, journals, and conferences. It also offers a variety of paper template choices, a variety of pricing options, and additional services to speed up the printing process.

10. OpenRead:

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