Top 3 AI Companies
AI technology is here for the long run. Once a niche market, now a considerable global powerhouse of innovation and digital development. Artificial intelligence is everywhere, winding its way through most sectors that are used within day-to-day society. Impacting the way in which we communicate, interact and develop with both machines and other individuals. Below are 3 are the AI companies who are taking the world by storm, changing the industry and developing a new normal.
Google’s AI platform is targeted towards Machine Learning Developers. Data Scientists and Data Engineers. It provides flexibility and support for other Google platforms. This includes the use of Google Cloud Storage with built in data labelling. Google Cloud holds a particularly strong offering that is a game changer in the race to be the top leading cloud server provider; offering Big Data, Analytics and Machine Learning. Gartner said that its "clients typically choose GCP as a secondary provider rather than a strategic provider, though GCP is increasingly chosen as a strategic alternative to AWS by customers whose businesses compete with Amazon, and that are more open-source-centric or DevOps-centric, and thus are less well-aligned to Microsoft Azure."
Amazon’s AI services emphasises the fact that no machine learning experience is required to make use of the processes they have to offer. Their machine learning capabilities have applications in fields such as video analysis, natural language processing and virtual assistants. The Amazon Echo has become one of the most well known devices that are used within homes and businesses across the globe. With this new intelligent technology, using AI applications, the Echo assists with turning on and off home appliances, whilst also being able to answer questions relating to location, weather and general knowledge questions. Through connecting with the internet and other external sources such as Spotify, Amazon has provided the world with a state of the art AI powered customer service, creating solutions to problems in real time.
Microsoft’s AI platform integrates with its Azure cloud product, enabling features such as image analytics, speech comprehension and prediction, with Microsoft’s solution claiming to be tailored to all stakeholders from data scientists to app developers and machine learning engineers. Due to a large majority of enterprises using Windows and other Microsoft services, and their ability to tightly integrate each with various applications, Azure makes the most sense to use for these organisations. This only increases the rate of loyal customers to Microsoft. Alongside any pro, there is a con, and in Microsoft’s case, Gartner reports on finding faults within their platform’s imperfections. "While Microsoft Azure is an enterprise-ready platform, Gartner clients report that the service experience feels less enterprise-ready than they expected, given Microsoft's long history as an enterprise vendor.".
The advantages and disadvantages of AI in cloud computing
Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.
We take a look at some of the benefits and drawbacks of AI in cloud computing.
The benefits of AI in cloud computing
A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.
AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.
Improved data management
AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments.
Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks.
With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly.
The drawbacks of AI in cloud computing
Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.
IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity.
While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.