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Take a look at the top 10 artificial intelligence repositories on Github.GitHub

GitHub has become increasingly popular in no time. This is one of the most popular platforms for coders and developers to host and share codes in a cooperative and collaborative environment. GitHub boasts millions of repositories in various domains. In this article, we will throw light on the top 10 artificial intelligence repositories on GitHub. Have a look!

TensorFlow has gained wide recognition as an open-source framework for Machine learning and Artificial Intelligence. This GitHub repository was developed by Google Brain Team and contains various resources to learn. With the state-of-the-art models for computer vision, NLP, and recommendation systems, you are bound to generate highly accurate results on their datasets.

This is a lightweight TensorFlow-based network that is used for automatically learning high-quality models with the least expert interference. This AI repository on GitHub boasts easy usability, flexibility, speed, and a guarantee of learning.

BERT (Bidirectional Encoder Representations from Transformers) is the first unsupervised, deeply bidirectional system for pre-training NLP. Evidently enough, this AI repository contains TensorFlow code and pre-trained models for BERT, aimed at obtaining new state-of-the-art results on a significant number of NLP tasks.

This Artificial intelligence repository focuses majorly on data processing. However, a point that is worth a mention is that Airflow has the opinion that tasks should ideally be idempotent. In simple terms, the results of the task will be the same, and will not create duplicated data in a destination system

This is a beginner-level AI GitHub repository that evidently emphasises document similarity. The idea behind the document similarity application is to find the common topic discussed between the documents.

AI Learning is yet another most widely relied upon AI GitHub repository that consists of many lessons such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing, to name a few.

This GitHub repository is an exclusive Machine Learning sub-repository that contains various algorithms coded exclusively in Python. Here, you get codes on several regression techniques such as linear and polynomial regression. This repository finds immense application in predictive analysis for continuous data.

This AI repository on GitHub is widely recognized across the globe as it contains classification, regression, and clustering algorithms, as well as data-preparation and model-evaluation tools. Can it get any better than this?

This GitHub repository has an organized list of machine learning libraries, frameworks, and tools in almost all the languages available. All in all, Awesome Machine Learning promotes a collective development environment for Machine Learning.

spaCy is a library foradvanced Natural Language Processingin Python. spaCy is that one repository that is built on the very latest research and was designed from day one to be used in real products.

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Top 10 Artificial Intelligence Repositories on GitHub - Analytics Insight

DLF Research added a research publication document on the Artificial Intelligence In Insurtech Market breaking major business segments and highlighting wider level geographies to get a deep-dive analysis of market data. The study is a perfect balance bridging both qualitative and quantitative information about the Artificial Intelligence In Insurtech market. The study provides valuable market size data for historical (Volume** & Value) from 2017 to 2021 which is estimated and forecasted till 2030*.

Some are the key & emerging players that are part of the coverage and have been profiled are Cognizant, Next IT Corp, Kasisto, Cape Analytics Inc., Microsoft, Google, Salesforce, Amazon Web Services, Lemonade, Lexalytics, H2O.ai.

Download Latest Artificial Intelligence In Insurtech Market Research Sample Copy Now @ https://www.datalabforecast.com/request-sample/388936-artificial-intelligence-in-insurtech-market

1. External Factor Analysis

An external analysis looks at the wider business environment that affects the business. This industry assessment covers all the factors that are outside the control. It includes both the micro and macro-environmental factors.

MACRO ENVIRONMENT: In-depth coverage of Factors such as governmental laws, social construct and cultural norms, environmental conditions, economics, and technology.

MICRO ENVIRONMENT: Factors highlighting the rivalry of the competition.

2. Growth & Margins

Players that are having a stellar growth track record are a must-see view in the study that Analysts have covered. From 2017 to 2020, some of the companies have shown enormous sales figures, with net income going doubled in that period with operating as well as gross margins constantly expanding. The rise of gross margins over the past few years directs strong pricing power of the competitive companies in the industry for its products or offer, over and above the increase in the cost of goods sold.

Check for more detail, Enquire about Latest Edition with Current Scenario Analysis @ https://www.datalabforecast.com/request-enquiry/388936-artificial-intelligence-in-insurtech-market

3. Ambitious growth plans & rising competition?

Industry players are planning to introduce new products launched into various markets around the globe considering applications/end use such as Automotive, Healthcare, Information Technology, Others. Examining some latest innovative products that are vital and may be introduced in EMEA markets in the last quarter of 2021. Considering the all-around development activities of companies, some players profiles are worth attention-seeking.

4. Where the Artificial Intelligence In Insurtech Industry is today

Though the latest year might not be that encouraging as market segments especially, Service, Product have shown modest gains, the growth scenario could have been changed if manufacturers would have planned an ambitious move earlier. Unlike past, but decent valuation and emerging investment cycle to progress in the Asia Pacific, North America, Europe, South America & The Middle East & Africa., many growth opportunities ahead for the companies in 2021, it looks descent today but stronger returns would be expected beyond.

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Insights that Study is offering :

Market Revenue splits by most promising business segments. [By Type (Service, Product), By Application (Automotive, Healthcare, Information Technology, Others) and any other business Segment if applicable within the scope of the report]

Market Share & Sales Revenue by Key Players & Local Emerging Regional Players. [Some of the players covered in the study are Cognizant, Next IT Corp, Kasisto, Cape Analytics Inc., Microsoft, Google, Salesforce, Amazon Web Services, Lemonade, Lexalytics, H2O.ai]

A separate section on Entropy to gain useful insights on leaders aggressiveness towards the market [Merger & Acquisition / Recent Investment and Key Development Activity Including seed funding]

Competitive Analysis: Company profile of listed players with separate SWOT Analysis, Overview, Product/Services Specification, Headquarter, Downstream Buyers, and Upstream Suppliers.

Gap Analysis by Region. The country break-up will help you dig out Trends and opportunities lying in a specific territory of your business interest.

Thanks for reading the Global Artificial Intelligence In Insurtech Industry research publication; you can also get individual chapter-wise sections or region-wise report versions like America, LATAM, Europe, Nordic nations, Oceania, Southeast Asia, or Just Eastern Asia.

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Artificial Intelligence In Insurtech Market to See Thriving Worldwide | Cognizant, Next IT Corp, Kasisto and more - Digital Journal

India's Science and Technology and Earth Sciences Minister Jitendra Singh and German Education and Research Minister, Bettina Stark-Watzinger, during their meeting in Berlin, expressed satisfaction on the ongoing science and technology Cooperation between the two countries, which is one of the strategic pillars of the bilateral relationship.

"There is a lot of scope to work together in Artificial Intelligence, for which experts on two sides have already met. An Indo-German call for proposals for this would be raised soon inviting proposals from researchers and industry," officials said.

The two countries have already started mapping each other's strength in areas such as application of Artificial Intelligence in Sustainability and Healthcare.

Both Ministers felt delighted that several initiatives for human capacity developments in science and engineering have recently been worked out, which includes Women Involvement in Science and Engineering Research (WISER) to facilitate lateral entry of women researchers into ongoing S&T projects and Paired Early Career Fellowships (PECF) creating an inclusive ecosystem for the Indo-German S&T cooperation with exchange of young researchers on both sides.

Stark-Watzinger supported the idea to further strengthen bilateral scientific cooperation by partnering in emerging science and technology areas where both Germany and India have strength to work together and serve two societies.

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India, Germany agree to work together with focus on AI, startups - ETTelecom

On any reputable website, one will find a chatbot to help the users. While interacting with the chatbot, the user engages themselves with a simulated human conversation instead of talking to a real human being.

A chatbot is a computer program or software that has the power to simulate human conversation both textual and voice conversation. The use of chatbots is extremely prevalent, especially in B2C and BCB websites.

Chatbot assistants on the website have many benefits like it reduces overhead costs of the company and providing support to the staff to handle the customer care service in a better way.

Chatbot uses Natural Language Processing NLP to function. NLP is a branch of artificial intelligence that powers the computer to understand both text and spoken words in a manner how human beings function.

Before understanding how chatbots work, it is important to know the three different types of chatbots. The different types of chatbots are rule-based chatbots, intellectual independent chatbots and AI-powered chatbots.

The rule-based chatbots are the simplest type of chatbots which give the users pre-defined options. Such chatbots are commonly visible in WhatsApp.

While engaging in such chatbots on WhatsApp, it is important to have a WhatsApp business account where the logo of the company serves as WhatsApp DP.

Also, WhatsApp Status can be used to keep the users updated about the products and services of the business. For such chatbots to function, the user needs to select one of the predefined options and on the basis of the option chosen by the user, the chatbot provides the solutions to the users.

These chatbots are particularly important to give frequently asked questions that many a time do not require actual human assistance. However, if the customer requires additional assistance, these chatbots are not quite helpful.

The second type of chatbot is an intellectually independent chatbot. These chatbots use machine learning to understand the inputs and requests of the users.

Machine learning gives the computer the ability to become powerful enough to teach itself from the data, recognize patterns and answer the queries of the customer without much human interference.

These chatbots are trained to identify specific keywords and phrases which then trigger the response of the chatbot. With more and more questions from the users, these chatbots train themselves to solve the queries of the customer.

For example, in such functioning chatbots, if the user types a problem such as, I want to know the ETA of the order. The bot will pick words such as, ETA and Order. By identifying these keywords, the chatbot gives predefined answers to the phrases.

The third type of chatbot is the AI-powered chatbot which is basically a combination of both the rule-based and intellectually independent chatbots.

These are the most powerful type of chatbot that simulates human intelligence. The artificial intelligence in such a chatbot is powerful enough to create an intelligent machine that has the ability to think like a woman.

The AI-powered chatbot is intelligent enough to understand free language. They do not need any predefined phrases to solve the queries of the users.

They are designed to understand both the preference of the user and understand the context of the conversation. On the basis of the requirement of the user, they can change the flow of the conversation.

To function in this way, they use machine learning, Natural Language Processing and AI to meet the requirements of the users.

These chatbots function by following different steps. In the very first step, the chatbot split the sentence written by the users in different parts which are also termed tokens.

Secondly, it divides the words used in the sentence in part of speech by identifying the words as adjectives, nouns and verbs, to name a few.

In the third step, the sentence is shortened only to contain the important words to produce a basic phrase. In the fourth step, the named entity is recognized by the chatbot.

In the final step, the chatbot engages in sentiment analysis to identify the mood of the user, which is extremely important to give better customer service to the user.

There are many benefits of using chatbots. For instance, it allows the business to handle multiple conversations in one. It saves both time and money for the company. The most important benefit of the chatbot is that it improves the customer engagement of the user.

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How Does Artificial Intelligence Work in a Chatbot? - Robotics and Automation News

Professor and Director for the Center of Applied AI at St. Thomas Dr. Manjeet Rege wrote a column for the Star Tribune on how AI can be useful for lawyers.

From the column: One sector, however, where many have doubted that AI could make a difference is critical thinking by lawyers in the courtroom. There are many ways, though, that AI can help the legal process without replacing lawyers. A lawyer who prepares adequately has more chances of winning their case, and AI can help with research. AI eliminates the arduous task of legal analysis by searching and discovering case numbers and main arguments of cases in bulk documents.

In addition, intellectual property law has many possible AI applications.

For instance, a key component of intellectual property is due diligence. Finding critical patents in a portfolio is a cumbersome task in which automation can be handy. AI technologies mimic the behavior of expert searchers by comparing competitor and market information with existing patents. In 2016, the due diligence for the acquisition of ARM by SoftBank for $31 billion was completed in a matter of days using AI systems, as reported by Forbes.

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In the News: Dr. Manjeet Rege on Artificial Intelligence and Law Firms - Newsroom | University of St. Thomas - University of St. Thomas Newsroom