AI & Machine Learning

The term Lean startup was coined about ten years ago. Since that time it has grown to become one of the most influential methodologies for building startups, especially those that fall in the category of web-based software companies. Lean came of age during the internet revolution. We now sit on the cusp of a different revolution — one ushered in by machine learning algorithms. It is safe to assume that most or all software in the near future will contain some element of machine learning. But how compatible is Lean with machine learning, in principle, and in practice? 

Analyzing past phenomena can provide extremely valuable information about what to expect in the future from the same, or closely related, phenomena. In this sense, these algorithms can learn from the past and use this learning to make valuable predictions about the future. While learning from data is not in itself a new concept, Machine Learning differentiates itself from other methods of learning by a capacity to deal with a much greater quantity of data, and a capacity to handle data that has limited structure. This allows Machine Learning to be successfully utilized on a wide array of topics that had previously been considered too complex for other learning methods.

Deep Learning requires a lot of computations. It typically involves neural network(s) with many nodes, and every node has many connections — which must be updated constantly during the learning. As the deep learning and AI fields have been moving extremely fast in the last few years, we’ve also seen the introduction of many deep learning frameworks. Deep learning frameworks are created with the goal to run deep learning systems efficiently on GPUs. 

Nowadays, there are so many tools out there that allow anyone to get started learning Machine Learning. No excuses! Machine learning can help us understand our world in ways we couldn’t otherwise. It can help us create and discover new things orders of magnitude more efficiently than ever before. You’ve got the power, use it wisely. The four foundation stones of Machine Learning are data, computations, algorithms, and education.

Radiology remains a solid career path, and AI will only serve to dramatically improve radiologists’ workplace conditions. While AI may take over certain tasks currently performed by radiologists, jobs in the field will remain abundant—and growing reliance on AI technology will only augment the other tasks that occupy a radiologist’s day. Given mounting workloads and the severe shortage of radiologists in the face of rising demand, AI augmentation will be a tremendous boon to the profession—not an existential threat. Here are four reasons today’s budding radiologists need not fear AI displacement.

Personalized or precision medicine is not a new concept, but our ability to implement it has just been significantly upgraded with a machine learning method called deep learning. Customized medical treatment requires multiple sources of data–genomics, electronic health records (EHRs) and medical imaging together. Perhaps even wearables and social data. Deep learning is accelerating our journey towards personalized medicine by sharing a few examples applied to EHRs. It is truly a transformative time for healthcare. Deep learning within EHRs can enable physicians to save more lives, reduce costs in the healthcare system. 

How to start Machine Learning and Deep Learning? Here, are curated list of some resources. Deep Learning definitely requires you to have a strong command of Linear Algebra, Differential Calculus and Vector Calculus, just to name a few. Be prepared to do all the Math. It can be a bit challenging but it will definitely be rewarding once you have gone through it and did your work. It would be hard for textbooks to capture the current state of Deep Learning since the field is moving at a very fast pace.

Today, rebounding from rationalist dominance and fuelled by big data, AI and medicine tend strongly to empiricism. This alignment threatens to entrench the incumbents in cycles of codependency and reinforcing errors. Examples of these cycles include data as a cure-all, theory-free science, and the misuse of statistics. A range of differences across medicine and AI may bring out the best that each community has to offer. Hype and disillusionment are to be expected a natural part of the process. But this isn’t another AI winter. These pendulum swings and ideological corrections are essential mechanisms of progress.

If your product or service isn’t working, you deliver the necessary assistance to your customers. To this end, you need an army of tireless, helpful employees and representatives to interface and interact with your influx of customers. In other words, your customer support channels need to be open and active nearly 24/7 to accommodate customer and audience demands. Chatbots are transforming how this process works, especially in regard to reliability and efficiency:. You might not believe it, but automated chatbots and messaging tools can supplement live support reps.

Foundation of trust is slowly fading as a new generation of AI-doctored videos find their way into the mainstream. Famously known as “deepfakes,” the synthetic videos are created using an application called FakeApp, which uses artificial intelligence to swap the faces in a video with those of another person. Educating people on the capabilities of AI algorithms will be a good measure to prevent the bad uses of applications like FakeApp having widespread impact—at least in the short term. We also need technological measures to back up our ethical and legal safeguards against deepfakes and other forms of AI-based forgery.

AI creates its own children that outperform humans, but this inaccuracy is all too often because AI researchers and AI practitioners do not participate in these debates. Using the terms such as “child” and describing the output of this network as smarter than what humans would design is sure to make for some good headlines. However, describing what happened as “algorithm uses a set of predefined rules to achieve a slightly better performance” is nowhere near as scary but it is probably more accurate.