AI & Machine Learning

Intelligent automation can help HR professionals make smarter decisions, help them get more done with less, and help HR shift its focus from manual, repetitive tasks to take on a more strategic role in the business – by innovating. Automation, learning systems, and artificial intelligence are all becoming key elements of HR practices and HR leaders and practitioners need to embrace these as ways to help your business achieve its goals. Intelligent automation can improve HR management in many ways.

AI has become so pervasive we often don’t even recognize it anymore. Besides enabling us to speak to our phones and get answers back, intelligent algorithms are often working in the background, providing things like predictive maintenance for machinery and automating basic software tasks. As the technology becomes more powerful, it’s also forcing us to ask some uncomfortable questions that were once more in the realm of science fiction or late-night dorm room discussions.

An important step while creating our machine learning pipeline is evaluating our different models against each other. A bad choice of an evaluation metric could wreak havoc to your whole system. So, always be watchful of what you are predicting and how the choice of evaluation metric might affect/alter your final predictions. Also, the choice of an evaluation metric should be well aligned with the business objective and hence it is a bit subjective. And you can come up with your own evaluation metric as well.

Machine Learning on Code (MLonCode) is a new interdisciplinary field of research related to Natural Language Processing, Programming Language Structure, and Social and History analysis such contributions graphs and commit time series. MLonCode aims to learn from large scale source code datasets in order to automatically perform software engineering tasks such as assisted code reviews, code deduplication, software expertise assessment, etc. Some MLonCode problems require zero error rate, such as those related to code generation. A tiny, single misprediction may lead to the whole program’s compilation failure.

AI systems and algorithms are created by people with their own experiences, backgrounds and blind spots which can unfortunately lead to the development of fundamentally biased systems. If we allow this incredible technology to continue to advance but fail to address questions around biases, our society will undoubtedly face a variety of serious moral, legal, practical and social consequences. It’s important we act now to mitigate the spread of biased or inaccurate technologies. Then what can be done?

Whether you’re a tech enthusiast or an industry leader looking to innovate, it’s impossible to miss the growing role of AI, machine learning, and deep learning. You probably know, for instance, that the data generated by the Internet of Things (IoT) is somehow connected to artificial intelligence, and that AI can work on that data to provide answers and insights. But we’ll bet that the concepts get pretty blurry at that point. AI represents a decisive break with the past, and it’s something everyone needs to understand.

The augmented reality (AR) will become the key interface between humans and machines that’ll help bridge the gap between the digital and physical worlds. AR comprises a set of technologies that superimposes computer generated digital data, images and animation on real world objects.  The technology is still in its early stages. Today, most AR applications are focused on entertainment and delivered through smartphone and tablet apps. But, they’re being increasingly applied to commercial and industrial applications. 

With the increasing adoption and business applications of AI and ML, many IT professionals are choosing AI and ML as their career. As an aspiring AI professional, you should ensure that you are always aware of any current and upcoming advancements in artificial intelligence. However, considering the competition to enter the domain, it may be difficult to stand out in a pool of applicants. This article discusses the skills and certifications you need to get hired as an AI developer, machine learning engineer, or a research scientist.

Despite the rapidly evolving market of AI hardware, of the three key parts of hardware infrastructure – computing, storage, and networking –  it is computing that has been the focus and has made significant progress in the last couple of years. The industry is going with the fastest available option and promoting that as a solution for deep learning. The other two areas, storage and networking, are still to be seen in the near future.

Mechanical automation reduces our need to move and that speaks to our appetite for convenience. Mechanical automation has slowly increased ever since the dawn of industrialization; hence we have gotten so used to the gradual change that we don’t really seem to care. Digital automation, on the other hand, is coming much quicker. That makes people much more worried. If you google “reliance on computers” you will get a lot of hits about all the dangers and effects of overdependence on technology and basically nothing about the benefits of relying on them. 

What are some of the trends that look most interesting within healthcare AI? One of the key trends is the use of health AI to spur the transition of medicine from reactive to proactive care. Machine learning-based applications will preempt and prevent disease on a more personal level, rather than merely reacting to symptoms. Ultimately, patients will benefit from timely and personalized treatment to improve outcomes and potentially increase survival rates.