Shamayita Acharya

The semiconductor industry is one of the quickest growing technology fields in the world right now. According to recently released data from the Semiconductor Industry Association or SIA, the sales in August 2018 for the sale of semiconductors around the world reached more than $41 billion. Compared to the data compiled by the SIA in August 2017, the global sales of semiconductors went up almost 15-percent in the course of a year alone. This is a very impressive increase in any type of industry, especially where tech is involved. Additionally, the Market Research Consultant (MRC) found that many popular semiconductor companies are already beginning to adopt IoT to grow their companies and revenue while beating out the competition. How Semiconductor Creators Are Adopting Big Data, AI and IoT To Improve Revenue It appears that the MRC is not the only company to figure the increase out. Another company, Accenture, is also agreeing to that fact as more and more semiconductor communit...

Big data is a hot topic that shows up somewhat regularly in all aspects of our lives. These days big data plays a role in nearly everything: the music we listen to, the GPS that guides us around new places, the healthcare system we are dependent upon, and so much more. Over 2,500 petabytes of data are collected daily and used to make our lives easier and more efficient. There is no doubt that the world wouldn’t have advanced as rapidly as it has without the advent of big data processing. But what many of us fail to grasp fully is the type of work and educational background that is required for professionals developing big data tools to keep coming up with advances. Many of us might think that a creative mind and the spirit of a designer is all that is necessary, but what matters more than all of that is a background in mathematics. Maths education is a critical component of the big data equation. It is used in nearly every aspect of the process, from big data tool design to data co...

Open source tools and technologies have become a significant part of IoT testing. This because they fuel the quick testing and integration of IoT platforms with various vendors. It is important to note that open source adoption is at the heart of DevOps provided that the majority of DevOps technologies and tools in the market are open source. This decreases the total cost of testing infrastructure and ownership. DevOps’s Open Source Ecosystem Assisting in IoT Testing Software developer in test (SDET), risk-based testing and shift left testing are few of the most sought-after IoT testing strategies. However, DevOps is one of the most effective strategies when you want to generate outstanding test efficiency metrics and coverage along with tangible cost saving due to its support of open source technologies and tools. Other than this, intelligent automation and DevOps is being used by companies to manage IoT at scale and attain quick innovation. DevOps also talks regarding the require...

Healthcare breaches pose a major problem for doctors, patients, and entire medical and insurance systems, exposing confidential patient information and leaving responsible parties vulnerable to lawsuits – and worst of all, these breaches are increasingly common. It raises the question: what can we do to protect healthcare data? New blockchain technology may hold the answer. Extending The Blockchain Blockchain systems are best known as the infrastructure underlying Bitcoin and other cryptocurrencies, but when it comes to cybersecurity, its function is to manage vulnerability and minimize available data via strong, standardized enforcement practices; by combining HIPAA and strict identity management, blockchain promises to engender greater stakeholder trust. Furthermore, while blockchain is being prioritized in traditional data security applications like records management, it can also be used to secure telehealth communications, in clinical trials, and tracing drug and medical devic...

“The world is changed,” said Galadriel in the Lord of the Rings. What the even queen could not foresee is the way the world could change. In her times magic was the only means to communicate and get information across the distance, now almost everyone has a smartphone equipped with sound wave recorders, cameras, acceleration gauges, gyroscopes, magnetometers, Wi-Fi internet and whatever manufacturers find attractive enough. We think that these technologies make our lives more comfortable and effective, and in this belief a new — Internet of Things — paradigm is born. What is the IoT paradigm? In IoT, the whole world is turned into a global computer, which gets data from everywhere. The processor works on big data in some form, while the output is considered to be crucial for the mankind to survive: to use energy more efficiently, to evolve as a civilization, to protect health, welfare, and offsprings. Such a shift in understanding the IoT not as a technology but as a philosophi...

Aviation, automobile and even train companies spend significant amounts of money annually reducing the weight of their products. Take the aviation industry, for example, millions of pounds are invested each year into R&D to reduce the weight of their plane’s components and structures while maintaining the relevant strength properties and functionality. The plane thus becomes more fuel efficient, it can carry more passengers and ultimately generates more profit. The same principles apply to the data in your business’ CRM and ERP systems. When such systems are burdened with unnecessary weight (duplicates and other low-quality baggage), they will not work as efficiently, effectively or as expected. It is therefore essential that the unnecessary weight that bad data applies to your business is eradicated. The removal of bad data can lead to significant advantages to your business such as: 1. More Trust in your Business Intelligence Important business decisions are o...

When we think of companies at the forefront of technological innovation, global financial institutions don’t necessarily come to mind. Instead, massive tech giants like Amazon, Apple and Alphabet dominate the conversation around innovation and garner the most attention. However, with new technologies like artificial intelligence going mainstream, even the classic gatekeepers in the financial sector can’t afford to ignore them. Now, the largest banks around the world are doubling down on their AI plays. Capital One — United States Coming in as one of the  top 50 best US financial institutions  on  Forbes US Best Banks  list,  Capital One has been beefing up its AI capabilities by prioritizing ethical AI in its systems.  Its Senior Research Manager on the Conversational AI team, Omar Flórez, a PhD researcher from Peru is one of the few Latin Americans studying the ethical aspects of machine learning. Although algorithms have the power to create huge effic...

By Lance Eliot, the AI Trends Insider The controversial Linear No-Threshold (LNT) graph has been in the news recently. LNT is a type of statistical model that has been used primarily in health-related areas such as dealing with exposures to radiation and other human-endangering substances such as toxic chemicals. Essentially, the standard version of an LNT graph posits that any exposure at all is too much and therefore you should seek to not have any exposure, avoiding even the tiniest bit of exposure. You might say it is a zero-tolerance condition (using modern day phrasing). Strictly speaking, if you believe the standardized version of an LNT graph, it means there isn’t any level of exposure that is safe. It is a linear line meaning that it goes straight along a graph (i.e., not a curved line, a straight line), typically at a steep angle like say 45 degrees, proceeding left-to-right, rising up to indicate that the more exposure you receive the worse things will get for you. This ...

Every year we talk about how  this  will be the Year of the Woman because of the incredible advances women are making. There’s no doubt that 2019 will go down in history as the first time we saw a record number of diverse women serving in the US Congress and announcing their candidacy for President of the United States. But you know what isn’t budging for the  12.3 million women US entrepreneurs  who founded companies? Access to capital. For a second year in a row, only 2.2% of investor funding went to women-led startups. #NotAcceptable. Despite all the media coverage and all the commitments by investors to diversify their portfolios, many continue to miss or ignore opportunities to invest in the next billion dollar startups that happen to be led by women. In our tech-driven economy, we’re still tackling incredible obstacles that could be resolved by backing the right creative and innovative minds. This continues to be the driving force behind our  Women Start...

 By Jodie Wallis, Accenture Canada’s AI Lead, and Amber Mac, technology reporter  The race is on for global industry leaders to take advantage of artificial intelligence (AI) and the wide variety of improvements it brings to virtually every business function. At the same time, concerns are on the rise about the potential social implications and unintended consequences that may come with machines making decisions for humans. So where does Canada — a country considered to be a global leader in AI research — fit into this exciting yet tumultuous landscape? Harnessing the power of artificial intelligence must be a top priority for Canada’s leaders. When Prime Minister Justin Trudeau joined us to discuss AI in Canada on Accenture’s podcast  The AI Effect , he asserted that if Canada wants to continue its leading role, our businesses and governments need to invest in moving beyond the research, experimentation and pilot phase to implement AI-powered solutions within their o...

One of the big misconceptions is that AI is a future technology, akin to humanoid robots that appear in television shows and movies. But AI is not a speculative technology—it already has many real-world applications, and ordinary people rely on it in one form or another every day. AI already powers many common mobile apps and programs. In fact, iPhones, Amazon’s Alexa, Twitter and Facebook feeds, Google’s search engine, and Netflix movie queues—to name just a few examples—all rely on AI. That said, there is a vast gap between the complexity of AI processing needed for the complex geospatial functions performed by self-driving cars versus, say, the basic AI algorithms used for more routine tasks like filtering spam emails. HOW IS AI BEING MISUSED? Technologist Melvin Kranzberg famously stated, “Technology is neither good nor bad; nor is it neutral.” AI technology follows this principle. It’s dual-use, meaning that it performs a range of functions, including both military a...

Recorded on December 12, 2018 To build an effective product that relies on machine learning, you need a large volume of high-quality training data. For the solution to correctly understand and mimic humans, it’s crucial to have a strategy around collecting and annotating training data that optimizes for quality. Join us to learn about the data you need to build solutions like natural language processing, chatbots, and sentiment analysis, with live Q&A to follow. In this webinar you’ll learn: The pros and cons of public data vs. building your own data sets How much time and energy to invest in data collection Why curated crowds yield higher-quality data for machine learning James Lyle, PhD in Linguistics,  Director of the Custom Linguistic Solutions team at Appen After earning his Ph.D. in linguistics at the University of Washington, James joined Microsoft in 1999 and spent more than 14 years working on various natural language technologies, including proofing tools, in...