The year-end and New Year's shopping blitz is lacking steam in Japan due to stalled goods production blamed on shortages of semiconductors and other parts and components. "A wide range of consumer electronics have been affected" by the parts shortages, an industry analyst said. Sony's PlayStation 5 video game console marked cumulative sales of 10 million units in July this year, only about eight months after it was released in November 2020, reaching the milestone faster than any other PlayStation model. But PS5 is now seen selling for over ¥100,000, double its regular price, on online flea markets amid a supply shortage. Last month, Nintendo Co. lowered the fiscal 2021 sales estimate for its Nintendo Switch game console to 24 million units from 25.5 million units.

Work-Bench calculated the percentage using its database of female-founded companies and information from financial data company PitchBook Data Inc., which shows a total of approximately 18,500 venture capital-backed business-to-business software startups in the U.S. Women have been historically underrepresented in the technology side of the software industry. U.S. Department of Labor data from 2019 shows that 18.1% of all software developers in the U.S. are women, and those women on average earn 88.7% of what their male counterparts earn. Things aren't easier for women who branch off to start their own enterprise software businesses, experts say. A combination of implicit biases, discrepancies in networking connections and a cycle that leaves women largely outside the venture-capital sphere makes it harder for female-led companies to get off the ground--perpetuating a stubborn gender gap in one of the country's most male-dominated industries. "Of course it's hurt me," Idit Levine, the founder and chief executive of Solo.io, said about being a woman in the enterprise software space.

WITH the cost of bringing a new drug to market now an average US$2.6bn1 and one-in-ten drug candidates failing to make it to market despite successfully completing Phase I trials2, it is no wonder that pharmaceutical companies have seized on the unparalleled data-processing potential of artificial intelligence (AI) systems. Their use in identifying compounds, some of which may have completed clinical trials already, that could be re-purposed to treat alternative diseases quickly and comparatively cheaply, is well documented. But as research scientists are beginning to find, AI systems are capable of achieving so much more. The potential applications of AI in drug discovery are almost endless, but one of the main areas of focus to date has been repurposing existing drugs. Typically, this involves finding new uses for drugs that have already attained market and regulatory approvals for the treatment of a specific disease.

Previously published on LSE Business Review. Machine learning is poised to pave the way for many exciting opportunities for businesses, but there are many hurdles to be crossed before getting to the finishing line. Many organisations are still struggling with legacy systems and are slow to invest in more advanced technologies. But the more pressing issue at hand, one that has been an ongoing problem for the technology sector, is the short supply of qualified talent to match what is a fast-moving and demanding industry. By design, machine learning is experimental and often unpredictable – a lot of exploration is required before organisations can even begin to make sense of the data and which machine learning algorithms will work best. While the unpredictable nature of machine learning is understandably daunting, many organisations have yet to fully grasp what is required to effectively deploy and manage it.

Machine learning is poised to pave the way for many exciting opportunities for businesses, but there are many hurdles to be crossed before getting to the finishing line. Many organisations are still struggling with legacy systems and are slow to invest in more advanced technologies. But the more pressing issue at hand, one that has been an ongoing problem for the technology sector, is the short supply of qualified talent to match what is a fast-moving and demanding industry. By design, machine learning is experimental and often unpredictable – a lot of exploration is required before organisations can even begin to make sense of the data and which machine learning algorithms will work best. While the unpredictable nature of machine learning is understandably daunting, many organisations have yet to fully grasp what is required to effectively deploy and manage it.

Two technology booms -- some people might call them frenzies -- are combining to turn a once-obscure type of microprocessor into a must-have but scarce commodity. Artificial-intelligence systems, made by companies ranging in size from Google to the Chinese startup Malong Technologies, rely heavily on a computer chip called a graphics processing unit, or GPU. The chips are also very useful in mining digital currencies like Ethereum, a bitcoin alternative riding the same wave of hype as its more famous cousin. With people and companies involved in the two surging tech niches buying up the same chips, GPUs have been in short supply over the past several months. Prices have increased by as much as 50 percent, according to some resellers and customers. "The chips are simply going out of stock," said Matt Scott, a technologist from the United States who founded Malong after leaving Microsoft's research lab in Beijing in 2014.

It is becoming easier, faster, and cheaper for companies of every enterprise to implement machine learning--a data-fueled artificial intelligence technology used to detect patterns and anomalies, and make predictions. Even though industries find its capabilities appealing, most companies are not yet taking advantage of this transformative technology. As explained in "Signals for Strategists: Machine Learning and the Five Vectors of Progress," Deloitte believes that progress in five key areas can help overcome the barriers to adoption and eventually make machine learning technology mainstream. According to a 2017 survey of 3100 executives in various sized companies across 17 countries, fewer than 10 percent of companies are investing in machine learning [i], despite it being considered "one of the most powerful and versatile information technologies available today."[ii] Deloitte points out some major factors hindering the adoption of machine learning: the short supply of qualified practitioners [iii]; the immature and still-evolving tools and frameworks for doing machine learning work [iv]; and the time-consumption and costs associated with obtaining enough data sets for machine learning model development.[v]

Machine learning – a piece of the artificial intelligence constellation – holds a lot of promise for enterprises, enabling programs and algorithms to become ever more intelligent. However, there's one problem: even the best-educated humans need more learning before they can understand machine learning. Bob Hayes, a professional data scientist and keen observer of all things data, picked up on a survey by Kaggle that finds that even data scientists still have a grasp on machine learning. The survey "revealed that a limited number of data professionals possess competency in advanced machine learning skills," says Hayes. "About half of data professionals said they were competent in supervised machine learning (49%) and logistic regression (53%). Deep learning techniques were among the ML skills with the lowest competency rates."

But it's not all magically going to occur overnight either. Pre-integrated systems based on converged and hyper-converged infrastructure are a critical first step. Machine learning algorithms require access to data to correlate and interpret events. Trying to aggregate data from IT environment made up of disparate components simply adds another layer of integration complexity to an already challenging task. Organizations that can more easily correlate events occurring across compute, storage and enterprise networks in context of their integration, in support of a workload are going to be able to significantly reduce their mean time to actionable intelligent IT operations.

Artificial intelligence has been dominating headlines for years, with doomsday cries about robots stealing jobs and Jetsons-esque predictions about space travel. But underneath all of that hype, machine learning has made huge advancements, and companies are hungry to reap the benefits, whether it be to power a new cybersecurity tool, create customized shopping experiences, or power better search capabilities. Of course, the first step for any company that wants to actually build or implement machine learning applications is finding the right talent. Data shows that machine learning is one of the most sought-after skills in tech. Combine that with the short supply of qualified candidates, and it's easy to understand why salaries in data science and other fields are steadily increasing.