By AI Trends Staff
The number one issue in lithium-ion batteries powering products from e-bikes and power tools for consumers, to self-driving cars and submarines, is to enhance battery safety, Dr. Rachid Yazami told an audience at the virtual International Battery Seminar from Cambridge EnerTech this week.
Dr. Yazami, founder of Singapore startup KVI, which is developing smart chips to enhance battery performance and safety, is known for his critical role in the development of lithium-ion batteries. In a talk on whether AI can help address battery issues, he outlined the challenges in a battery market that is projected to reach $35 billion in value by 2025. “The market is growing very fast,” he said.
Short circuits in lithium-ion batteries are caused when a thin slip of polypropylene that keeps electrodes from touching is breached, so that the electrodes come in contact and generate heat and possibly fire.
“We don’t want to see vehicles and mobile phones catching fire,” Dr. Yazami said. “These things we have to address very seriously. A lot of progress has been made, especially in increasing the quality of the batteries.”
Additional challenges include: reducing charging time currently ranging from 1.5 to eight hours for an electric vehicle depending on the battery, to less than one hour and even 30 minutes; increasing the driving range from 250 km to 500 km (150 to 300 miles) currently, to 900 km (560 miles); and extending the battery service life, currently at about two years, to 10 years.
In its work to address these challenges, KVI has developed two kinds of chips, one that combines material science with AI to “manage the battery performance in smarter ways,” and the other that helps to manage a new protocol for fast charging. Eventually the chips may be combined, “to get all the advantages.” Dr. Yazami will be looking for manufacturers to embed the chips in new batteries once they are available, which he estimated would be in 12 to 18 months. “We already have working prototypes for ultrafast battery chargers,” he said in response to a question from AI Trends.
The fast-charging chip can measure data on entropy, a property of a thermodynamic system, to assess the state of the battery and of its safety. “We can adapt the charging protocol according to the state of charge of the battery, Dr. Yazami said. “We use the AI to optimize the data processing and the protocols we use to charge the battery,” allowing for optimal charging, not more than the battery can take. The system continues to learn as the battery is used and is aging.
One of the biggest reasons for fires in lithium-ion batteries are internal short circuits, “which happen when the separator breaks or there is a hot spot and it melts,” Dr Yazami said. “That can trigger events that end in a thermal runaway, sometimes fire and explosions.” Detecting this thermal runaway at an early stage is very difficult.
“We have developed some technology to detect it at a very early stage,” he said. He has found a linear relationship between battery entropy, charted on the Y axis, and battery enthalpy, a thermodynamic quantity equal to the total heat content of the system, on the X axis. “We have found very specific cell voltages where we can trigger an alarm.” This allows the researchers to adjust the charging voltages based on the state of health of the battery, using AI to help. “We are developing the software to follow the battery as it is aging.”
The team has also developed a non-linear fast charging solution, in which current and voltage is not constant, but adapts to the battery.
“We have developed solutions based on AI and thermodynamics data to monitor the state of battery safety,” Dr. Yazami said.
Asked by another researcher how he measures entropy and enthalpy for state of charge (SoC) detection, Dr. Yazami said the linear relationship depends on the chemistry and state of health of the battery. ”The linear coefficients evolve as the battery ages. The AI enables prediction of the coefficients to determine the state of health of the battery,” he said.
Learn more about Dr. Rachid Yazami.
Impacts of Artificial Intelligence in Content Writing
Artificial intelligence has transformed the content-writing industry because most things are done by artificial intelligence when writing content today. Many publishers use artificial intelligence to generate their content, for example, The Press Association creates about 30,000 local stories through artificial intelligence.
“If you think that this is done by formula writing, you are wrong, because the AI has gone beyond formulating and is now creating sensible written content.”
Whether you are a content writer or have a content writer team, writing content is not an easy task, especially if you have limited time. There are also many other tasks such as keyword research, optimization, proofreading, uploading and publishing.
Now, all these tasks take time and effort, but what if there is a machine that does these tasks automatically for you?
In this article, you will learn more about how AI works and some of its applications in the content writing industry.
How AI Content Writer works
There are various technologies in AI Content Writer, the most important of which is Natural Language Generator (NLG). NLG automatically generates the narrative written content from the data you have provided, i.e. titles, key points.
NLG will be responsible for various tasks for writing content such as
- Data analysis and reporting
- Automatic e-mails
- Financial update system
- Automatic communication between user and system
- Business intelligence dashboards
- Business intelligence dashboards
These are only the most common and popular, but numerous other applications are used in different industries.
However, we have sorted out some of the popular AI-based tools available online to help content creators.
Every author of the content knows the importance of plagiarism and the side effects of its use in publications. Artificial intelligence has made it easier to check for plagiarism in your content because without it, it seems impossible to verify copied content.
The plagiarism AI checks your article and compares it to all other content to find similarities. However, the modern AI of some tools is also able to detect the paraphrased context in your article, as CopyScape’s plagiarism check does.
The above tool is a very accurate gauge of the appropriate content, as it searches all content in search engines, including descriptions on social media and YouTube. If your content has any twisted content, it will detect it.
“There was a time when authors had to hire proofreaders to find bugs in their content, but now the trend has changed: the AI-based tools automatically detect the bugs and suggest you correct them.”
Grammar checkers like Grammarly only take seconds to detect grammatical errors and some other content errors. It uses its AI technology to find all tenses, spelling, interference, clarity and output errors.
In this way, the tool could be more efficient than a human because it only takes seconds and does not need any revision. Some of the modern artificial intelligence in this tool help improve the integration, clarity and execution of sentences.
The paraphrasing tool is another example of an AI-based tool that works more efficiently than a human. The online rephrasing tool works by replacing the words with their suitable synonyms to create a new copy of the original content.
As an example of how it works, we can take the paraphrasing tool from Prepostseo. This tool is very fast and precise as it uses its AI-based technology Natural Language Processing NLP to replace the words but keeps the actual meaning of the context.
Image to Text Converter
This online tool uses AI to convert the uploaded image into text that can be used for any purpose. This tool is useful for students, teachers, business people and writers as they need to convert previously written work into editable documents.
For example, Simple OCR’s picture-to-text tool uses the AI-based Optical Character Recognition OCR technology to extract the text from the image. This tool is beneficial for converting documents that need to be converted into some modifications, and you don’t need to rewrite the entire context.
AI article generator
“This is also called a virtual content writer because it generates the article independently without human interaction and writes the content automatically with the support of the AI”
Article Forge’s article generator is a very amazing tool that not only creates context but also makes it suitable for SEO, foreign exchange, review, editing and proofreading your article.
Reverse Image Text
Sometimes you need to search for a query about a similar image or similar information through an image. There are some tools on the Internet known as reverse image text that perform the aforementioned operations.
The user uploads the image and the tool looks for the relative result over the image and in this way, you can get what you want. As an example, we can take the tool called Tin eye, which helps to browse the data within seconds by uploading images.
If you are a writer or SEO specialist, you may know the importance of text optimization. Text optimization is described as improving your performance in the context of improving readability.
The writing performance check tools are based on artificial intelligence because it analyses the article and generates a score accordingly.
The author can write the content in an easily understandable way and this is one of the needs of SEO guidelines for search engines.
The tools that generate the readability value analyze your text and see the reading behaviour according to the human.
The content writing quite becomes difficult when trying to escape plagiarism or low-quality writing but the AI has changed this practice. Now, the time efficiency has gone more far after the AI has started generating articles on its own.
If you are a writer and want to check the quality of your writing then AI will let you know your position. It is expected that AI will be enough in the future to replace humans for writing the content.
Also, Read How Artificial Intelligence is Transforming Education
How chatbots add value in the financial industry
In the world of technology, the first big leap was reached, and it was bringing the web to the mobile. Now the next one should take place, bringing the mobile to the conversation. At this stage, it can be stated that the first generation of chatbots in the financial industry has not yet convinced the majority of customers. However, the better the technology becomes, the more banks provide a wide range of roles and functionalities for these virtual assistants. The simple question and answer programs available today will transform themselves into intelligent conversational partners who can help customers to manage their financial businesses. Accordingly, financial institutions expect that more and more customers are managing their financial businesses by using chat services such as Facebook Messenger and WhatsApp.
The development of sophisticated chatbots and their widespread acceptance by the consumers could take several more years. As soon as it happens, however, the idea of managing finances without a bot will be like trying to imagine life without a smartphone. Most of the FinTech start-ups are already one step ahead and it is time to catch up for traditional financial institutions. Bot and artificial intelligence (AI) technology provide finance services the tools they need to improve the customer experience, streamline the compliance process and lower costs.
Improvement of customer experience
These days, organizations have a lot of opportunities to meet and interact with their customers. There are sufficient communication channels such as Facebook Messenger, e-mail newsletter, SMS and mobile apps that companies can use to engage with their customers or promote their products and services. The most important aspect to consider is how to use these various communication channels to ensure that businesses get more value out of each one. On the one hand, it is difficult to manage the entire process, even more for financial institutions as their structures are more complicated. On the other hand, customers are more and more looking for exceptional services from their banks and also want a highly personalized experience where banks are able to remember their preferences, monitor their expenditures and advise on savings.
Organizations can simplify the process by using bots while they are not required to develop or modify their services for each communication channel. Besides, they have the ability to gather information about the audience that helps financial institutions create a personalized customer experience. Furthermore, in general, customer service agents can only support one person at a time whereby chatbots can support thousands of customers at the same time. Thus, agents can devote themselves to more complex tasks. On the whole, these bots can provide excellent services and experience to customers, resulting in higher customer satisfaction and revenue growth and this 24/7.
Revenue opportunities & lower service costs
Not only the existing processes are optimized, but new business ways are also being opened up that increase the opportunity for revenue growth. A business can receive significant web traffic due to online advertising expenditures. However, it may not be converted into tangible sales. Nonetheless, by implementing a chatbot, a customer gets prompted when browsing something specific, even if the user has no intention to buy. It is not only the advertisement but also the virtual consultation regarding offered products and services that add value for customers. All this, around the clock one can offer at no additional cost. At this stage, it can be stated that this marketing and sales strategy can increase the revenue and at the same time lower the service cost.
Why Data & Data Annotation Make or Break AI
Everything in this universe is captured and preserved in the memory, in a large scale we can refer to it as Databases. Before we can proceed on how Data can make or break AI, let’s see what Data Annotation is. Data annotation is the process of appending important data to the original data. This dataset is without form or clarity at the beginning phase and therefore it is ambiguous to computers. Data without identifiers is just chaos, for a machine learning algorithm.
However, this chaos can be converted into a structured training program by annotation which has an effect all the way up the queue. Let’s go back to our Search Engine scenario to explain this. The IAI integrated Technology must include a dataset of text samples annotated for entity extraction in order to create an entity extractor. Fortunately, there are a bunch of different ways of tagging also within attribute selection which will help to educate the system for marginally multiple tasks.
Data annotators build metadata that defines or categorizes data in the form of code snippets. In the past, businesses used data annotation to define structures and allow data easily accessible. Now although, companies are concentrating their efforts on data annotation to optimize data libraries for structured ML or unstructured ML learning programs.
Creating metadata to the program is a straightforward process however, there is more to explore while annotating data in preparation for educating a machine learning or artificial intelligence algorithm. Your Machine learning model should be just as reliable as the annotation from its knowledge findings. We have classified the annotation into two segments, namely Instance and Semantic segmentation.
Let’s discuss on Instance Segmentation vs. Semantic segmentation. Instance Segmentation of instances is the function of identifying and quantifying each distinctive object in an image that exists in an image. Semantic segmentation is distinct from instance segmentation, i.e. various elements in the same class may have unique features as in-person A, person B, and thus color variations. The image below shows rather crisply its differences between instance segmentation and semantic segmentation.
Algorithms for machine learning do not just arise out of nothing. They need to be shown what an entity is before they can isolate or connect any specific element. They must know what to call them, when and how to. In general, they require preparation.
To do something like this, programmers depend on massive, human-annotated datasets, created for a task given from millions of instances of the right interfaces. Through testing each data point numerous times into the software, a framework can be constructed that has derived the complicated framework of rules and relations behind all the given data.
Therefore, the context of a database describes the limitations of the ability of an algorithm, whereas the amount of detail it provides helps to decide the sensitivity with which software can fulfill its mission. There must be an unbroken connection among high-quality data and high-performance software, and huge data value which will offer the added dimension to a system.
In addition, there are tons of open-source, off-the-shelf data available on the web to which many businesses dig out to extend their repositories. There has not been much support for those who are trying to create a sleek-of-the-range system.
In NLP, there is a need to keep up with language’s rapid expansion will easily create publicly available redundant. Active in application technology or AI gigantic? Over the next four weeks, we’ll take a close look (and interesting!) at the infrastructure that enables standard search to click.
Consider the expression “North West.” Its perception was obviously a place’s northwest until some years ago. North West is now as likely to apply to the daughter of Kanye West as it would not be referring to any geographic area.
Those implicit context changes occur across time, in any culture, and identity on the earth. The current language would be old news for a few months. Words or phrases are being developed, old ones are being redesigned, and cultural trends are rising and declining. Meanwhile, the difference in information across data from fifteen years ago and today’s data source is expanding into a coastline.
The only way to keep enjoying the wave of support is just to switch to human experts, who are fluent in the cultures and languages that the software must learn. Being the only credible source of ground-breaking reality for language-based algorithms, human intelligence is the hidden power behind the best training examples and the finest machine-learning by augmentation.
Within this segment let us just dig deep into the NLP production process. We will discuss how professional data providers create and manage the machine learning natural resources required to help all the above-mentioned technologies and devices. And therefore let ‘s gain a little bit of methodology initially. To truly understand this segment of the production chain, recognizing how data annotation functions are important.
The data sources that annotators commence with having to suit a certain profile and will also decide how often data must be annotated. The optimum design framework has the main characteristics. This should be comprehensive, describing the language, structure, and style of the document that you wish to bring into the framework of Named Entity Recognition (NER).
This should be regulated, including circumstances of every other type of entity that should be collected from the process. For example, a system could not learn to remove major corporations if the training data provides enough reference to large corporations.
That should really be clean. Handling a bunch of Html files during preparation certainly would not give better results. If the site will be in a different language, instead of identifying symbols is especially essential. In this scenario, “é” could be a peculiar class or “e” letter including dialect. Standardizing every instance of all this ensures the model doesn’t really distinguish among characters that are virtually the same. Maintenance is extremely significant in languages such as Japanese, which has both a “full-width” as well as “half-width” form of katakana scripts and Unicode.
This should suffice. In it to be reflective you need a certain amount of data and get enough references for each form of an object. It guarantees consistency and is key to setting a golden benchmark that will measure the efficiency of the program.
These alternative techniques generate different combinations of input-output inside the data. Since machines generalize the regulations surrounding a database from the configuration of such combinations, inserting significantly different parameters to the textual data will result in simulations that are configured for an entirely different type of job.
Phrases or sentences are marked according to context through this direct textual data which could be used to educate the element generator model. Names should be labeled as Names, while corporations should be marked as Corporations, etc. These tags come from a grading system which can extend to various levels, based on the extent of specifics the client requests.
There are several other ways of marking a text, but we will avoid making an extensive description just for the sake of precision. Certain machine-learning functions like emotion interpretation or image processing other than attribute abstraction often get their own set of special annotation approaches.
Though the instance earlier may seem clear, it isn’t simple to create a clean, oriented AI training dataset. There are indeed many activities that need to be measured in order to create successful training data. Most of these could consume precious time across vast sections if done by anyone who is not an expert.
Not everyone is able to translate a sentence into chains of requirement. Indeed, it can be a huge hassle to find effective annotators. And this is one of the simpler aspects of the process, in many cases.
When a community of annotators is formed, there is a whole series of activities to be done behind the scenes to manage. There seems to be a tremendous amount of secret work involved in annotating, from reviewing, onboarding, and maintaining tax enforcement to delivering, overseeing, and evaluating the performance of project activities.
Putting this sort of device out is a challenge for everyone. Consequently, tech firms also opt to delegate to enterprises specialized in data annotation. They free up time and effort by bringing qualified external participants into the project to get on with what they’re doing best to build browsers.
When you educate these models or indeed any ML system with incorrectly classified data, the results will also be inconsistent, inaccurate and will not give the user any value.
Text and internet search:
By marking concepts inside the text, ML models may start to interpret what users are really looking for page by page but taking into consideration a human’s motive.
Data annotation will give chatbots the capabilities to react to a question accurately, whether it is vocalized or typed.
Natural language processing (NLP):
NLP programs can start to interpret a query ‘s context and produce a smart response.
Optical character recognition (OCR):
Data annotation enables computer engineers to develop educational programs for OCR systems capable of recognizing and translating character recognition, PDFs, and text images or words.
ML models can understand to interpret words that are voiced or penned between one language into another.
Evolving self-driving vehicle innovation is a great example of why it’s important to educate ML systems correctly to understand photos and videos and interpret things.
Software engineers are developing algorithms to identify cancerous cells or other X-ray, ultrasound scan, or other clinical data deviations.
Like humans, AI algorithms require additional real-world knowledge which may involve more data generated by the actual world’s own trials and errors of simulations. Moreover, judging AI solutions in the initial stages while they still had no or little knowledge will be inappropriate and entirely inaccurate. That was one of today ’s most popular mistakes and usually leads to dissatisfaction and misinterpretation about the maturity of models surrounding the AI. We need to give time to learn for AI-powered applications and be carefully tested before implementing them in the business.
Impacts of Artificial Intelligence in Content Writing
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