Google Cloud and BT have established a five-year strategic agreement to help BT speed its digital transformation. 
The artificial intelligence (AI) and machine learning (ML) platform provider cnvrg.io, an Intel subsidiary, has announced the availability of cnvrg.io AI Blueprints. The new offering intends to let developers quickly produce AI apps with easy-to-use ML pipelines that can operate on any infrastructure. 
Nokia has debuted its new Intelligent RAN Operations solution, which uses machine learning (ML) to handle the rising complexity of 5G networks. In 1959, Arthur Samuel derived the term of machine learning and defined it as a field of study that provides computers the ability to learn without the addition of explicit coding. Machine learning enables computers to think and learn from their surroundings using various data models as well as trial and error algorithms. The term ‘machine learning’ is self-explanatory as it provides the computer with the ability to think and make decisions by using various data training and testing algorithms, thus giving the ‘machines’ the ability of ‘learning’ and having control of driving conclusions based on the trained data. The addition to machine learning can work great wonders in cyber security.
Machine learning is a branch of artificial intelligence, which is a sophisticated technology that performs tasks without human help. The algorithms are designed to learn from experience and better perform at complex tasks. Netflix’s recommendation system is a good example here.
The programmers or data scientists choose the type of machine learning based on the data they are dealing with. The four types include supervised, unsupervised, semi-supervised, and reinforcement.
Algorithms in supervised machine learning are trained based on the labeled datasets which in turn classifies the data and derives the results accordingly. Supervised learning is task-driven.
Example: Filtering out spam emails.
Algorithms Used: Naive Bayes algorithm, support vector machine(SVM), etc. are some examples.
Datasets are not labeled in this one and the algorithm analyzes the datasets and looks for the presence of any pattern in them. Unsupervised learning is data-driven.
Example: Image recognition.
Algorithms Used: K-means clustering, probabilistic clustering, etc.
It is a balance between supervised and unsupervised machine learning algorithms and uses a mixture of both labeled and unlabeled data sets. The semi-supervised learning model explores its understanding of the data itself.
Example: Audio and video manipulation.
Algorithms Used: Self-trained naive Bayes algorithm (natural language processing).
Learns the environment on its own and doesn’t use the sampled datasets. Reinforcement machine learning algorithm operates on the trial and error method. Bad outcomes are rejected and the system uses the correct generated results for training.
Example: Policy creation.
Algorithms Used: Q-learning.
Activated in 2014, Sophia is the first humanoid robot. She is smart and can not only express facial expressions but can also use humor in her words. Sophia was first launched publicly in Texas. She was also granted citizenship by Saudi Arabia. In the not too distant future, robots will be able to perform daily regular activities in the same manner that humans do in real life.
Machine language is imperative to realize the dream of a digital world, giving decision-making control to machines and providing ease to human beings from working on tardy tasks. Apart from just being a helper in daily work, machine learning can also provide benefits in the department of cybersecurity, which is the most present-day demand by the institutes working on confidential data. It is imperative for companies to have a secure system that protects their confidential records and prevents any attempts of hacks. Data breaches of, for example, company profiles can significantly damage their reputation, lead to financial loss and even fines. The introduction of machine learning in cyber security can help solve the security issue present in organizations today.
The future of cyber security lies in advanced technologies like machine learning. With machine learning, we can add security systems trained to recognize the patterns and improve the security systems based on the generated outcomes. Artificial intelligence can cover the loopholes and the hidden ambiguities in cybersecurity, with advancements like facial recognition, voice recognition, eye recognition, and fingerprint recognition, etc. Moreover, technologies like an identity verification system and know your customer kyc compliance can also provide security control on login checks to stop unauthorized users from accessing and corrupting the system.
The addition of identity verification can provide the security of the confidential information needed at the current moment. An identity verification system authenticates the user before giving them access to their system. Identity verification solutions authenticate the identity of customers during onboarding. The verification documents presented by the consumer are verified for their originality. The user’s identity is verified with biometric verification services like Facial recognition that can use 3D mapping and analyze the skin texture along with detecting the liveness of the user.
The chairman of Google, Eric Schmidt, says that the press and media have immensely covered the innovation of self-driving cars and robots whereas the real future of the company lies in machine learning. Machine learning gives computers the ability to become smarter and more personal.
To wind up the whole conversation, machine learning is important, not only for digitizing the world but also for enhancing the current cybersecurity precision and improving the safety protocols of organizations.
The post Machine Learning – What It Can Bring to Cyber Security? appeared first on SiteProNews.
While Artificial Intelligence (AI) and Machine Learning (ML) are on the radar of practically every enterprise, much work has to be done to fully realize their promise, according to a new study conducted by Coleman Parkes Research. 
Webscale, a company powering modern commerce with a SaaS platform that leverages distributed systems, cloud, automation, machine learning, and DevOps protocols, has announced the release of Webscale CloudEDGE CDN. 
Silicon Valley company Tekion, creator of a fast cloud-native automotive retail SaaS platform, has partnered with Microsoft to provide new capabilities for automakers using Microsoft Azure. 
AWS re:Invent in Las Vegas was an exciting event. In addition to the sheer pleasure of gathering in person again with innovators across sectors, AWS made a series of announcements. Read the Expert blog by Rahul Subramaniam. 
KubeSphere, a fast-growing open-source community, has released KubeSphere 3.2.0, the most recent version of a complete Kubernetes management platform. 
Alluxio has announced the immediate availability of Data Orchestration Platform version 2.7. Alluxio 2.7 offers enhanced performance insights and support for open table formats, including Iceberg and Apache Hudi. Insurance is as old as human civilization. There are records of a method of insurance called bottomry dating far back to 4000 BCE. This was a type of transaction where a ship was used as collateral by a captain to buy supplies on credit. If the ship or boat got destroyed by natural causes at sea, the lender had to take the situation as his or her loss. Lenders eventually began studying weather patterns to determine the likelihood of the captain returning with his boat unharmed. This was an early use of predictive analytics.
Insurance has grown much since the days of the bottomry and so has predictive analytics. A major aspect of this growth is the amount of data used by modern insurance providers to perform predictive analysis.
Predictive analytics is a branch of analytics that involves the use of models and statistics to predict future events. It is not shocking, then, how important predictive analytics is in insurance.
History is riddled with the use of predictive analytics. Even the use of seers in folklore was an attempt at predictive analytics. Predictive analytics was used by the Lloyd company in 1689 to determine the premium that would be paid in exchange for voyage insurance. The company used data of past voyages to calculate the possible risk of insuring a new one.
In World War II, Arnold Daniels used predictive analysis to ensure there were no casualties in the war. Daniels eventually went on to create the Predictive Index (PI) and it was subsequently adapted for the workspace.
Analytics progressed into computer use in the 1960s when computer science became mainstream. It was discovered that computers could be programmed to build models for predictive analytics, and so their use for this purpose skyrocketed. In the past, insurance companies employed few variables to determine their premiums, but as the collection and use of data expands, more advanced predictive analytics have been employed in insurance.
Premiums, terms, and conditions are traditionally calculated using few linear variables. It is a straightforward method that employs a single formula for all scenarios of that product.
Taking health insurance as an example, a fixed premium might be set for all forty-year-olds who purchase a particular insurance plan. This one ‘premium for all’ method might incur higher payments for the insurance company because there might be people who have a family history of developing type 2 diabetes in their forties. This means that such clients would make more health insurance claims than the average healthy person in their age group.
The implications are that the clients without chronic health issues will eventually pay premiums for clients with chronic health issues and not get any insurance benefits for being healthy. Clients who understand this might reconsider maintaining their premiums since there is no incentive for not using their insurance.
There is only so much data that a human can analyze without help from a computer. As computer use has become more complex, so has predictive analytics. Complex predictive analytics, a staple in the portfolio of data analytics firms, is a method in which many variables are used to build complex models for predicting possible future events.
With the explosion of internet use, there is an equally sized explosion of data. Using these data streams, machine learning can be employed to determine more complex future scenarios as regards insurance.
For example, going back to health insurance, let us imagine a scenario where two forty-year-old men purchase the same plan. The company, employing advanced predictive analytics, discovers that one of them is genetically predisposed to prostate cancer. It means he will make more claims than the other individual. The company, based on this piece of information, gives the genetically predisposed forty-year-old a slightly higher premium to account for the potential cost in oncology department visits.
Complex predictive analytics, however, isn’t without challenges. Incorporating complex predictive analytics, which usually involves machine learning, into existing insurance pricing might make the whole insurance marketplace less transparent and confusing. Predictive analytics that take in more variables might result in more accurate risk assessments, but it will not matter how accurate they are if the assessments can’t be explained, or justified, to the customers.
However, if implemented right, complex predictive analytics will yield better outcomes for insurance businesses.
Machine learning has been tied so deeply to predictive analytics that in certain articles they are used interchangeably. In reality, they are quite different. Predictive analytics is using the past to predict possible outcomes of the future. Machine learning, on the other hand, is a branch of artificial intelligence and computer science that gives computer systems the ability to use data to learn and improve.
Machine learning, because of its structure, is vital in predictive analysis. Decision trees, neural networks, and regression can be programmed into a system, and then data fed to it helps the system predict possible future events. These future events can range from weather patterns to natural disasters, economic depression, and the possibility of a pandemic up to the marketability of a product.
The importance of predictive analytics cannot be over-emphasized. In insurance, it is particularly important because:
Incorporating predictive analysis into insurance is the present and the future, but if done wrongly, can be detrimental to the sustainability of the business.
Most businesses outsource complex predictive analytics because building the structure and hiring the workforce required is difficult and expensive.
When there is an existing pricing method in an insurance company, predictive analytics has to be incorporated in a way that puts into consideration the existing pricing structure. This is because a sudden overhaul of pricing might discourage existing customers from retaining subscriptions.
Insurance companies, unlike finance institutions, have been more conservative in incorporating technology into their operations. However, that has begun to change. The importance and benefits of predictive analytics in insurance are becoming more obvious, making the need to embrace it more obvious as well.
Machine learning is no longer a future plan but a present reality. It will shape the future of insurance, changing the ways the industry operates.
As long as there is data, and computers to analyze it, there will be machine learning. Therefore, predictive analytics has become and will continue to be a core aspect of insurance services.
The post The Critical Role of Predictive Analytics in Insurance appeared first on SiteProNews.
Palo Alto Networks’ new Next-Generation CASB utilizes cutting-edge technologies like machine learning and natural language processing to discover new apps and monitor for potential security issues. 
Oracle has released its Oracle Cloud Infrastructure (OCI) AI services, a set of services that would make it easier for developers to incorporate AI into their applications without the need for data science knowledge. 
With the release of the EOS Network Data Lake (NetDL), Arista has announced the next major development of the Arista EOS network stack. 
Learn more about AI → https://www.ibm.biz/Bdfu29 Watch “What is NLP (Natural Language Processing)?” lightboard video → https://youtu.be/fLvJ8VdHLA0 Explore what’s next in AI → https://www.ibm.biz/Bdfu2k Check out IBM Watson → https://www.ibm.biz/Bdfu25 In this lightboard video, Stacey Gifford a Chemist with IBM Research, explains how we can use artificial intelligence (AI) to develop new materials to help […] 
cnvrg.io Metacloud, OS for artificial intelligence and machine learning, is now available for exclusive early access on request. 
Amazon Web Services (AWS) has announced the public availability of Amazon Elastic Compute Cloud (Amazon EC2) DL1 instances, a new instance type for training machine learning models. 
Global content delivery network (CDN) and cloud provider, G-Core Labs, has launched its first cloud Point-of-Presence in Germany, in the city of Frankfurt am Main. 
Chip manufacturer NXP Semiconductors, a company counting 29,000 employees in more than 30 countries with revenues of $8.61 billion in 2020, has chosen Amazon Web Services (AWS) as its preferred cloud provider. Disclosure: We are a professional review site that receives compensation from the companies whose products we review. We are independently owned and we have spent time and effort doing our best to offer you the most relevant and accurate information. All the "customer reviews" are posted by real webmasters (hosting customers) and we're not responsable for their opinions. Purchasing any service from the hosting companies listed on our site is your solely decision.
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