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Take A Look At The Top 5 AI Companies With A Market Share Analysis

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Take A Look At The Top 5 AI Companies With A Market Share Analysis

As the scope of artificial intelligence is growing, top organizations are in a race to achieve AI supremacy

As the scope of artificial intelligence is growing, the big tech organizations are investing, a huge amount of money, in the research and development of AI solutions. These organizations are in a race to achieve AI supremacy. In this chapter, we will explore the top 5 companies that lead the arena of AI.

 

Amazon Web Services

The company’s cloud service remains the widely utilized cloud service and accounts for 35% of the company’s revenue. AWS helps customers to build cloud platforms with increased flexibility, scalability, and reliability, with advanced computing power, database storage, and content delivery.

With the help of its AI algorithm, Amazon enhances customer experience, optimization in sales, and improves operations. Its machine learning model Alexa communicates with the user, by formulating smart answers by leveraging natural language processing. The company’s Amazon echo acts as an assistant and is governed by smart speakers.

 

Google

The company has transformed from a mobile- tech giant to an AI-tech giant. With AI technology integrated into almost all its applications, be it Google Maps, YouTube, Google Search Engines, or Google photos it provides a customer experience. The company leverages technology such as Google Cloud, and Deep Learning for storing the user’s data and analysing the users’ habits especially while recommending the videos. Deepmind, is an AI company acquired by Google which was formed in 2010, is leveraging machine learning for bringing new ideas into the field of healthcare, mathematics, and computing infrastructure.

The company’s Google Assistant is a chatbot that utilizes Natural Language processing that can be connected to a phone, laptop, and speakers to either make calls or search for a topic. It has recently introduced, Google Duplex, which is an AI-driven technology that helps in making appointments and Google lens, is governed by facial recognition and enables to search images online.

 

IBM

IBM is leading the artificial intelligence world. With its AI technology IBM Watson, it is utilized in all industries. IBM Watson is a data analytics processor that leverages natural language processing for analyzing human speech to understand the meaning of human speech and syntax. With its data analytics capabilities, it enables for processing of large amount of data, human speech, and answering the question which was not possible earlier. With its machine learning capabilities to process the new data that is entered into the repository, IBM Watson delivers insightful outcomes to the users. Even though it is deployed in almost every sector of the industry, the main focus of IBM Watson is on its services for cybersecurity. One of the key attributes of IBM Watson, as mentioned by IBM is to clear out unstructured data and documents by learning the subjects and pairing answers and questions. It can process large documents and generate human-like audio with the help of IBM Watson Text to Speech.

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Microsoft

From making a series of Window processors for the computers, to heavily investing in the research and development of AI, Microsoft’s contribution is equated in both the evaluation of computers and that of Artificial Intelligence. The company’s Microsoft Cortana, utilizes natural language processing to communicate with the users and Microsoft Bing utilizes image recognition techniques for recognizing images. The company’s cloud platform Microsoft Azure deploys intelligent cloud services for seamless operations to the customers. The company’s Microsoft 365 leverages data analytics for providing meaningful insights to the customer. In 2019, Microsoft funded the US$1 billion to the AI-research lab OpenAI, to research and explore the possibilities for amalgamation of GPT-3 and Microsoft Azure, for developing super-computing capabilities.

 

Facebook

Facebook has reformed from a social network site to an organization whose prime focus remains on artificial intelligence. It leverages AI technologies such as Facial Recognition, Language Translation, and machine learning algorithm, amongst others to improve the user’s experience. It leverages AI for thwarting the cases of Suicide, by scanning the posts and comments, and identifying which indicate depression and sadness.

The company’s research group Facebook AI Research (FAIR), is working on technologies such as conversational AI, Natural Language Processing, and Computer vision, to provide breakthroughs in AI technology.

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Watch Out For The Top Market Opportunities Enabled By AI

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Watch Out For The Top Market Opportunities Enabled By AI

Integrating AI will redefine a company’s product roadmap, what else? Let’s find out

Like many new technological breakthroughs, the full potential of AI will also be realized slowly, and it only takes someone exploring the possibilities around them to find out something new. Its potential has already delivered significant advantages to businesses at large creating huge opportunities for the future. AI offers vast opportunities for automation and digital transformation. It won’t eliminate jobs anymore right now; it will upgrade them. Eliminating some of the menial, monotonous and repetitive tasks will free up human employees to apply more creativity and human judgment.

A majority of organizations that have already begun the transformation to AI now are looking for ways to obtain repeatable value. They are continuously going to be better placed to maintain their competitive edge over their peers. Credited to its huge advantages, many businesses have already started hiring devoted professionals to have their own AI-based apps. Artificial intelligence can drive a significant return on investment by managing and maximizing businesses’ marketing strategies. It can help monitor, analyze and understand customer data across all the channels, assisting in better decision making.

Embedding AI throughout the enterprise will considerably allow business leaders to automate, stimulate and enhance key business processes to transform at scale. It will also enable them to keep track of what a business’s competitors are doing. Successful implementation of AI products will perform more for a company. As our future is likely to rely on AI, a person should spend less time looking at screens while computers do things for him/her automatically. Integrating AI will also redefine a company’s product roadmap.

It is expected that AI will bring more personalized conversational interfaces that make a person’s interaction with a computer less stressful by making it feel more like a conversation with a friend or colleague. These conversations can be held through voice or text, just like human-to-human conversations.

Most importantly, using AI will be beneficial in taking advantage of large amounts of online as well as offline information to make informed, data-driven decisions that will make a business grow. AI-driven tools can be fitted into every data-producing workflow and deliver insights that are quite applicable and actionable as well.

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Why Small Data Failure Will Result In Major AI Project Collapse?

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Why Small Data Failure Will Result In Major AI Project Collapse?

Beware of major reasons for small data failure to make an AI project successful in future

Big data is dominating the global tech market and transforming it into a data-centric world in recent times. Cutting-edge technology models and projects of artificial intelligence need data to drive meaningful insights to earn profit while having a deep understanding of human behaviour. Working on an AI project is essential for effective data management and to look out for small data failure. Yes, a small data failure can lead to a massive collapse of an AI project because data is the essential or key element in that. Small data failure can lead to cost huge losses of millions of dollars in a company. Thus, let’s explore some of the top reasons for an AI project to collapse because of a small data failure.

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Small data failure is directly proportional to an AI project failure

It is known that an AI project involves a majority of data from primary research that provides small data due to the time-consuming process. Data scientists or other data professionals may have a deeper knowledge of the artificial intelligence algorithms behind AI project models. But there can be a lack of the importance of assumptions behind working on any technique. An AI project largely depends on data from different sources in the form of structured, unstructured, and semi-structured data. Thus, for effective data management, big data helps in providing the relevant data that can be used for training purposes. If there is any small data failure like negligence or potential error in collecting and reporting the necessary data, it can lead to AI project failure. The unsuccessful AI project will not provide an accurate prediction or meaningful in-depth insights to drive profit in the nearby future.

Small data failure can create a drastic effect on the data management of an AI project due to the involvement of necessary information. This includes information from CRM, customers, sales, customer behaviour, customer satisfaction, customer engagement, and many more. these factors are all important for a successful AI project. Smart functionalities of artificial intelligence depend on the relevant information for effective data management for the betterment of the target audience.

One of the key reasons for a small data failure is the lack of data quality. This means that the output of an AI project depends on the quality of relevant data. There are potential opportunities that the business predictions and recommendations as the output can become wrong or inaccurate. Thus, this can be one of the reasons for an AI project to collapse because of a small data failure.

An AI project includes the integration of artificial intelligence and big data to provide the necessary outcomes. It can be a failure if the big data consists of some small data failure due to outdated, duplicate, missing, or messed up data for the training purpose. If it is no a clean data management then it will force an artificial intelligence project to be a failure.

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That being said, data professionals must have proper data management through an effective data strategy to manage the key elements of an AI project with the integration of big data efficiently. Data governance can lead to the potential elimination of a small data failure which can remove the chance of getting AI project collapsed.

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Top 10 AI Stocks To Buy After Selling Your Cryptocurrencies

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Top 10 AI Stocks To Buy After Selling Your Cryptocurrencies

These top 10 AI stocks will bring you a lot of profit after your cryptocurrencies are sold.

After selling your cryptocurrencies, you must be looking for the most profitable stocks to invest your money in. Well, AI stocks are currently the hottest in the market as their software solutions are being used in almost every industry. From start-ups to business giants, everyone is interested in implementing AI into their business operations. Here are the top 10 AI stocks you can buy for a better profit after selling your cryptocurrencies.

 

NVIDIA Corp

Nvidia’s data center business represents a steadily increasing share of the company’s total revenue. This segment isn’t all AI-related — Nvidia’s graphics cards are used to accelerate a wide variety of data center applications. But Artificial Intelligence is one of the driving forces behind the company’s growth. High-end chipmaker Nvidia is providing the massive processing power advanced AI applications will need. One of the fastest supercomputers, Leonardo, is powered by Nvidia graphics processing units.

 

International Business Machines Corp.

IBM’s strategy with AI is to apply the technology in ways that augment human intelligence, increase efficiency, or lower costs. In the healthcare industry, IBM’s AI technology is being used to create individualized care plans, accelerate the process of bringing new drugs to market, and improve the quality of care. In the financial services industry, via the company’s 2016 acquisition of Promontory Financial Group, IBM is using Artificial Intelligence to help clients with the daunting task of financial regulatory compliance.

 

Alphabet Inc

Google and YouTube parent company Alphabet uses AI and automation in virtually every facet of its business, from ad pricing to content promotion to email spam filters. Alphabet has AI and machine learning advantages across its product range, and Artificial Intelligence could help the company further fine-tune its industry-leading online advertising business over time.

 

Micron Technology, Inc

Micron Technology manufactures memory chips, including dynamic random-access memory (DRAM) and NAND flash memory found in solid-state storage drives. Most of what the company makes are commodity products, meaning that supply and demand dictate pricing. In the future, demand for memory chips will only grow, and that’s especially true in the Artificial Intelligence industry. Self-driving cars are a good example. All the sensors and cameras produce a lot of data around 1 GB per second, according to Micron estimates. Data centers running AI processes need plenty of memory and so do smartphones that may be doing AI work.

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Microsoft Corp.

In 2020, Microsoft announced the construction of a new supercomputer hosted in Azure, Microsoft’s cloud computing network. The supercomputer was built in collaboration with OpenAI LP to train AI models with the ultimate goal of producing large AI models and related infrastructure for other organizations and developers. In late 2021, Microsoft also debuted Context IQ, an Artificial Intelligence application that can predict, seek and suggest information for employees.

 

Amazon.com Inc

Amazon uses artificial intelligence for everything from Alexa, its industry-leading voice-activated technology, to its cashier-less grocery stores, to Amazon Web Services Sagemaker, the cloud infrastructure tool that deploys high-quality machine learning models for data scientists and developers. Amazon’s e-commerce business is also built on Artificial Intelligence since algorithms run its top-flight recommendation engines for e-commerce and video and music streaming. AI is how Amazon determines product rankings.

 

Meta Platforms Inc.

Facebook parent company Meta Platforms has already applied AI technology to its news feed and advertising algorithms, drawing heat from critics who say the company prioritizes engagement and conflict over user safety and societal stability. Meta is now shifting focus to applying AI technology to developing the metaverse, a digital world in which users interact in an immersive virtual environment.

 

C3.ai Inc

C3.ai is a SaaS company whose software allows companies to deploy large AI applications. The company’s tools help its customers accelerate software development and reduce cost and risk, and they have a wide variety of applications. For example, the U.S. Air Force uses C3 AI Readiness to predict aircraft systems failures, identify spare parts, and find new ways to increase mission capability. European utility company Engie (OTC: ENGIY) is using C3 AI to analyze energy consumption and reduce energy expenditures.

 

NICE Ltd.

NICE is a leading provider of software applications that manage call center operations and customer interactions. NICE’s AI and machine learning technology also streamlines fraud detection and regulatory compliance. The company’s AI helps businesses categorize and analyze voice communications, leveraging the power of data analytics to improve business interactions with contact center teams.

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DocuSign Inc.

Electronic signature and document automation specialist DocuSign has been investing heavily in AI technology to make contracts and documentation more efficient. Contract AI can help businesses automatically sort through millions of pages of contracts, court cases, and regulatory provisions and easily flag potential sources of legal problems, privacy issues, security vulnerabilities, or even regulatory breaches.

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