Jun 3, 2021

Price trend prediction software Cryptohawk.AI launches

AI
Cryptohawk
Bitcoin
Cryptocurrency
2 min
This new software has a more modern interface, updated algorithm, and improved charting to provide crypto investors with valuable tools

DigiMax Global, a company that provides artificial intelligence (AI) and cryptocurrency technology solutions, has announced the official launch of CryptoHawk.ai. 

CryptoHawk.AI allows anyone to use the power of AI and machine learning (ML) for improved investment returns in crypto. Using the software’s AI-based predictions, investors can reduce risk, remove stress and save time all while taking advantage of volatility when investing in Bitcoin (BTC) and Ethereum (ETH). 

According to DigiMax Global, CryptoHawk.ai is different from any other AI prediction system on the market due to its sophisticated deep learning ML engine. It uses market-leading data collection and handling processes across the Big Data 4V principles:

1. Volume: massive amounts of data;

2. Variety: numerous relevant sources of data;

3. Velocity: high speed of processing; and

4. Veracity: removal of bias, noise and outliers.

CryptoHawk.ai synthesises, evaluates, and organises data to discover new patterns, anomalies, relationships, and real-time trends that are delivered to investors' phones. To further assist investors, CryptoHawk.ai has been modulated with a new threshold logic to send alerts for meaningful price swings in order to ensure that users do not get overwhelmed by alerts and exchange fees during times of extremely high volatility.

“The comprehensive suite of tools and learning-AI is shaping CryptoHawk.ai in to a must have tool that can allow investors to profitably capture volatility instead of being fearful of such volatility.” said Damon Stone, ex-Merrill Lynch professional trader and SME supporting cognitive modeling and training of the CryptoHawk.ai.

Improved Investing with the power of AI

Cryptocurrency investors are consistently dealing with time-consuming tasks such as watching charts, indicators, reading articles, and spotting trends on social media, all tasks that make it difficult for them to maintain and grow their portfolios. CryptoHawk.ai simplifies much of this process as it analyses millions of data points per hour through a proprietary ML algorithm that continuously spots relevant patterns, makes decisions, and generates accurate price trend predictions. CryptoHawk alerts investors through email and text message when a price trend changes allowing users to act with confidence.

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

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

 

Lower costs

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.

Deeper insights 

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. 
 

Intelligent automation 

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. 

Increased security 

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

 

Data privacy 

 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.

Connectivity concerns 

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.

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