AI & Machine Learning

With all the hype surrounding AI we tend to overlook a very important fact. The best defense against a potential AI cyber-attack is rooted in maintaining a fundamental security posture that incorporates continuous monitoring, user education, diligent patch management and basic configuration controls to address vulnerabilities. In the security world AI has a very clear-cut potential for good. The industry is notoriously unbalanced, with the bad actors getting to pick from thousands of vulnerabilities to launch their attacks, along with deploying an ever-increasing arsenal of tools to evade detection once they have breached a system. All explained in this article.

As the technologies become more developed, the union of AI and intelligent personalization could lead to the emergence of companies that are predicting your dream vacation before you have any idea of your own. The futuristic chatbot will already know your preferred airport, dates, times, mode of transport and your travel companion from your emails, Facebook, Twitter and calendars — if you grant the required access, of course.

Convolutional neural networks, also called ConvNets, were first introduced in the 1980s. The early version of CNNs, called LeNet could recognize handwritten digits. CNNs found a niche market in banking and postal services and banking, where they read zip codes on envelopes and digits on checks. Convolutional neural networks are composed of multiple layers of artificial neurons. One of the great challenges of developing CNNs is adjusting the weights of the individual neurons to extract the right features from images.

What businesses can provide to their customers in the future determines whether they will succeed or not. The ever-changing market trends aim to fulfill every customer demand and influence business to do the same. Using the best approaches of technology- AI, machine learning, big data analytics and more, it becomes easier to deprive what customers want and spend resources accordingly to produce the best results. In times, where every business is expected to transform its operations online, the implementation of AI concepts becomes crucial to produce the best results. 

Those companies that can put machine learning models into production, on a large scale, first, will gain a huge advantage over their competitors and billions in potential revenue. But, there is a huge issue with the usability of machine learning — there is a significant challenge around putting machine learning models into production at scale. Organisations can create incredibly complex machine learning models, but it’s problematic to take huge datasets, apply them to different iterations of ML models and then deploy those successful iterations into production.

At first, an AI president sounds like a preposterous idea. But is it really? People already trust AI to drive their cars and airplanes, manage their bank accounts, and make medical diagnoses. Would it be such a big leap to entrust the governing of the country to an AI as well? An AI president wouldn’t be susceptible to emotions and personal desires, as it would have none, and it would be able to make better, fairer, and more rational decisions than its human counterparts.

Next week, the California Consumer Privacy Act (CCPA ) will go into effect. It really hasn’t gotten much attention–but it should. The law is likely to have a far-reaching impact on the tech world, especially in categories like AI (Artificial Intelligence). So what is the CCPA? Actually, it is the most thorough privacy regulation in the US. It even goes beyond the requirements of the General Data Protection Regulation (GDPR) act, which is focused on Europe. 

The future of computation looks like it will involve speeding up computations to handle the relentless and exponential increase in data production. However, speeding up individual processors is difficult for various reasons, and Moore’s law cannot last forever — it is becoming increasingly constrained by the limits of heat transfer and quantum mechanics. A greater push will continue to be seen towards parallel computing, especially with more specialized hardware such as GPUs and TPUs, as well as towards more energy-efficient computing which may become possible as we enter into the realm of neuromorphic computing.

Will intelligent robots replace doctors in the future? It is still a difficult question to answer. Most medical startups are aimed only at simplifying and optimizing the medical flow process, and they do not provide for the replacement of a doctor with a robot. In addition, machine learning takes time, because we need to teach the neural network to “see” the problem in the image or to operate as a doctor. Despite all the difficulties, AI-driven medical projects have prospects.

Between 2015 and 2019, the openings for positions like machine learning engineer grew by 344%. Technically speaking, it’s a branch of artificial intelligence, based on the idea that a system can learn from data, identify specific patterns and make decisions without any human intervention. The truth is that machine learning has several applications that are just becoming realized. Therefore, it’s no surprise that the employer demand for talent with such skills is growing with each day.

The new world we live in gives us more help — and more doubts. Machines stand behind everything — and the scope of this everything is only growing. To what extent can we trust such machines? We are used to relying on them in market trends, traffic management, maybe even in healthcare. Machines are now