In this new era of remote working, our reliance on technology is greater than ever, with many businesses having migrated processes from offline to online to accommodate the transition. But amongst the success stories of companies adapting to this change, working in the current climate has raised a pertinent issue for employers: how can they deliver effective learning and development (L&D) to employees when they are scattered across different workplaces? Irrespective of the shift to working from home, providing personalised and efficient L&D has been an issue for quite some time. We are in an age where we are more informed than ever, and while this is no bad thing, one of the drawbacks of this knowledge explosion is that employers are now faced with the prospect of ever-changing policies, procedures and regulations. It is vital that staff are kept up-to-date with industry developments, but this is becoming an increasingly unattainable goal.

For about 50 years, researchers have strived to predict how proteins achieve their three-dimensional structure, and it's not an easy problem to solve. Google's Deepmind claims to have created an artificially intelligent program called "AlphaFold" that is able to solve those problems in a matter of days. The latest version of DeepMind's AlphaFold, a deep-learning system that can accurately predict the structure of proteins to within the width of an atom, has cracked one of biology's grand challenges. According to John Moult, Professor at the University of Maryland, "It's the first use of AI to solve a serious problem,". In the experiment, DeepMind used a new deep learning architecture for AlphaFold that was able to interpret and compute the'spatial graph' of 3D proteins, predicting the molecular structure underpinning their folded configuration.

"How can I solve my problem with AI?"- As Machine Learning and Artificial Intelligence reach more and more areas of daily life and enter all economic sectors, this question is often asked by decision makers eager to integrate AI into their business. While AI can offer great gains to businesses, in the following, you will see why jumping in with such a question is not an appropriate approach. Before diving into AI for your business problem, a well-defined business strategy must be established and the question of "Why should I use Machine Learning/Artificial Intelligence?" should be thoroughly considered. Being able to answer that question requires having the exact definition of the business problem: knowing the available data and desired output, having a plan for testing, monitoring and improving your solution, and being clear about the end use-case. After all, it's no use having a perfectly designed model from the data science team if you haven't planned how the rest of the company can use your model outputs.

There are, largely speaking, two camps when it comes to artificial intelligence. On one hand, there's those who are wary of the negatives that have been reported on in the press and worry that the future isn't as bright as we'd hoped for. Then there's those who see the remarkable opportunity it offers and are working hard to mine its advantages. Undeniably, AI will shape society, it's unavoidable; history's not exactly littered with examples of technological advancements that have simply been rejected. While the tightrope of personal intrusion needs to be very carefully walked, it would be remiss of us to reject its benefits outright.

Although Einstein himself made many advances in physics, from special and... [ ] general relativity to the photoelectric effect and statistical mechanics, there were many problems he couldn't solve during his life. How much better could AI have done? At the dawn of the 20th century, there were a number of crises in physics. Radiating objects like stars emitted a finite, well-defined amount of energy at every wavelength, defying the best predictions of the day. Newton's laws of motion broke down and failed when objects approached the speed of light.

Quantum physics phenomena is one of the hottest topics in contemporary physics. It looks at how particles in nature "come together" and bring along their unique properties, such as electrical conductivity or magnetism. However, it has been almost impossible for even the most seasoned researchers to get more than a glimpse of these complex phenomena. This is because of the enormous number of particles these phenomena contain (over one billion billions in each gram) and the enormous number of interactions between them. A new study published in Physical Review Letters by Prof. Shashua's computer science doctoral students at Hebrew University -- Yoav Levin, Or Sharir and Nadav Cohen -- has demonstrated mathematically that algorithms based on deep neural networks can be applied to better understand the world of quantum physics, as well.

Under the direction of Mobileye founder Amnon Shashua, a research group at Hebrew University of Jerusalem's School of Engineering and Computer Science has proven that artificial intelligence (AI) can help us understand the world on an infinitesimally small scale called quantum physics phenomena. Quantum physics phenomena is one of the hottest topics in contemporary physics. It looks at how particles in nature "come together" and bring along their unique properties, such as electrical conductivity or magnetism. However, it has been almost impossible for even the most seasoned researchers to get more than a glimpse of these complex phenomena. This is because of the enormous number of particles these phenomena contain (over one billion billions in each gram) and the enormous number of interactions between them.

Diversity and inclusion are intrinsically linked to a company's business strategy. As more companies set audacious goals for the diversification of their workforce, talent analytics can support subjectivity with objective data-driven decisions that will make diversity goals a reality. There is no doubt that more diverse and inclusive workforces have a competitive advantage over those that do not. In fact, diversity and inclusion are intrinsically linked to a company's innovative strategy, enabling a more creative and agile organization. It is no wonder that one of Gartner's top 10 predictions for IT organizations in 2019 is that more than three-quarters of larger enterprises will set their diversity and inclusion goals toward a 2020-2022 timeline by the end of this year. As more companies set audacious goals for the diversification of their workforce, the spotlight will start to shift to their real-world metrics and whether they are actualizing their goals.

The Fourth Industrial Revolution (or industry 4.0) is the new wave engulfing the business world. Whilst technologies in the space of 3D printing and artificial intelligence are gaining traction, a question hovers over the enterprise resource planning (ERP) domain. Historically, ERP's had re-invent themselves many times over to stay in the game of advancing technology solutions. This time, the challenge facing ERPs is different: two problems that need to be solved simultaneously. The conundrum is that the solution of one problem works against resolving the other.

Although Einstein himself made many advances in physics, from special and general relativity to the photoelectric effect and statistical mechanics, there were many problems he couldn't solve during his life. How much better could AI have done? At the dawn of the 20th century, there were a number of crises in physics. Radiating objects like stars emitted a finite, well-defined amount of energy at every wavelength, defying the best predictions of the day. Newton's laws of motion broke down and failed when objects approached the speed of light.