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Needed: Discoveries to feed green economy – www.mining-journal.com

A paradox of the mineral exploration sector is that it is both distinct and inseparable from the broader mining industry. Although the odds of a greenfields exploration project ever becoming a mine have been estimated at around one in one thousand, every mine begins life as an exploration project.

With this in mind, explorers should always be mindful of the challenges facing the industry. Inthecoming decade, there will be no two greater challenges than the need to consistently demonstrate strong sustainability credentials, including appropriate environmental, social and governance (ESG) principles and practices, and to meet the growing material needs of the world economy and emerging green economy.

ESG: An opportunity, not a hindrance

More projects than ever are failing to advance notbecause of technical issues, but because of environmental and social issues. It is incumbent on the exploration sector to address sustainable work practices, including ESG, early in project life cycles rather than set it aside for when projects reach a development stage. Integrating sustainability into decision making right from the start of an exploration project provides the foundation for constructive long-term engagement with all stakeholders, thereby serving to significantly de-risk a project. Therefore, sustainability should be viewed not as a hindrance to exploration but as an opportunity.

Establishing a respectful, open, engaged and supportive relationship with the local community, where shared values can be established, is critical to establishing a sustained social licence to operate (orexplore). While part of the engagement may includehighlighting the types of activities involved inexploration, and thereby highlighting the differencesbetween exploration andmining, there are many other aspects to this engagement - responsible behaviour, being open to considering alternatives (e.g.,non-invasive versus invasive activities, moving around properties, etc.), looking forcollaborative opportunities, understanding the Needed: Discoveries to feed green economy rights and perspectives of various stakeholders and maintaining open, regular and effective communication, just to note a few.

Establishing a positive and constructive track record at the early stages builds trust and respect andhelps lay the foundation should discovery and exploration success occur. Establishing success in this area, coupled with discovery success, should lead to better outcomes with investors (and by extension, exploration funding).

Uptake of non-invasive and lower impact, more environmentally sustainable on-site technologies will serve to make projects more environmentally friendly and assuage fears from communities who may only know about mining from negative depictions in film or the wider media.

These non- to less invasive technologies include:

Drones, in low impact acquisition of geophysical or other remote sensing imagery;

Passive-geophysics, techniques that allow for a greater understanding of subsurface geology and structure without the use of disruptive seismic or electrical sources;0

Non-invasive geochemical surveys, most surface geochemical surveys are relatively non-invasive, but the use of technology such as ionic geochemisty allows for rapid anomaly detection with the smallest of impact possible;

Deep 3D geophysics inversion modelling, utilising new geophysical techniques to assess deep signatures coupled with using geological models to assist in constraining them during inversion modelling;

Remote sensing and hyperspectral platforms, greater use of these tools early on to guide exploration ensures fewer areas require invasive exploration; and

Downhole monitoring, using downhole tools and probes to maximise data capture and utilise every borehole to its full extent.

Greater adoption of non-invasive technologies offers the added benefits of reducing costs and improving technical efficiency-KPIs that will only grow in importance as explorers go to increasingly greater depths to discover the minerals needed to fuel the post-COVID economic recovery and assist in the decarbonisation of the global economy.

Better technical efficiency reduces risk

The exploration sector is also a rare case of an industry that becomes harder the more successful we become. As the number of viable deposits near surface dwindles, especially in well-established mining jurisdictions, explorers need to go undercover in search of new discoveries, increasing uncertainty and risk. This will increase our reliance on new technology and geological concepts such as system science. This is where technical efficiency and effectiveness becomes critical.

Adoption of non-invasive technologies like those mentioned above will help. So too will new invasive technologies such as coil drilling, which reduces the cost and increases the speed of drilling, allowing us to drill at greater depths where the next Tier 1 deposits are more likely to be found. Coil drilling technology also provides an added benefit by reducing the drilling footprint and thereby minimising environmental impacts. Couple this with new lab-at-rig technology and other developments like portable XRFs and PhotonAssay and we have the potential to dramatically speed up mineral exploration.

Data science, and specifically machine learning, is another important growth area for the exploration sector and mining industry in general. Today, our industry collects vast quantities of data, too much forany geologist or team of geologists to efficiently analyse in a reasonable timeframe. A machine learning model can be trained to examine reams of data - whether that be proprietary company data and/or historical pre-competitive data in jurisdictions where such a thing is available - in a much shorter period of time. It is important to note however, that machine learning is not a silver bullet; it performs the grunt work for the geologist, much like a paralegal does for an attorney. But it is also much more than that, having, for example, the ability to potentially detect connections between disparate data sources that would be virtually invisible to the human eye.

Discovery success is not just about having a large geological team with access to these technologies. Better efficiencies will also require the adoption of certain organisational principles. Having a technically strong team is fundamental to exploration success, asRobert Friedland has demonstrated with Turquoise Hill Resources and Ivanhoe Mines and others have shown with various exploration ventures. Coupling technical capabilities with a strong team culture has also been a successful combination, as the leaders of Western Mining Corporation (later acquired by BHP) knew in the late 1960s to 1990s when they made numerous discoveries including Kambalda and St Ives in Western Australia and the giant Olympic Dam copper-uranium deposit in South Australia.

Finally, the need to improve technical efficiency demands new ways of thinking. One potential new approach is scenario planning, a strategy of planning for multiple future scenarios pertaining to one's business. Building on this concept, researchers at theUniversity of Western Australia's Centre for Exploration Targeting are developing a multiple hypothetical reserves' approach whereby explorers would develop multiple scenarios about currently undiscovered mineral accumulations that could be extractable in the future.

Discovery rates must improve

Countless observers have noted that the continued growth of the world economy and particularly low-carbon technologies like batteries, wind turbines and solar panels will require much larger quantities of certain commodities - such as copper, cobalt, lithium and graphite - than can be produced from existing (or known) resources and reserves. It is for the exploration sector to meet this growing need from the new green reality, and to do so, our success and discovery rates must improve. Together with adequate funding this will require the sector to embrace and successfully integrate new technologies and new mindsets with sound geological knowledge and thinking.

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Cryptocurrency prices today: Bitcoin, Ethereum, other coins recover from lows – Livemint

Cryptocurrency prices today have recovered from lows after the government listed a Bill that seeks to prohibit all private cryptocurrenies, barring a few exceptions.

The world's largest cryptocurrency Bitcoin is up by 4.59% at 45,60,417 (in INR terms), while Ethereum is trading 6% higher at 3,47,661, according to the data from CoinSwitch.

In dollar terms, Bitcoin was trading at $58,560.80 with a market capitalisation over $1.1 trillion. Dogecoin price is up over 4% to $0.22 whereas Shiba Inu surged nearly 6% to $0.000040, as per CoinDesk.

The government has recently confirmed that it will introduce The Cryptocurrency and Regulation of Official Digital Currency Bill, 2021" during the winter session of Parliament beginning 29 November.

The bill seeks to prohibit all private cryptocurrencies in India, but will allow certain exceptions to promote the underlying technology of cryptocurrency and its uses, the government said in a notification on Lok Sabha website. The bill also aims to create a facilitative framework for creation of the official digital currency to be issued by the Reserve Bank of India".

Amid lightning pace developments in the cryptocurrency space in the country, the industry has urged investors to remain calm and not arrive at a rushed conclusion.

"It is hard to comprehend what the government means by private cryptocurrencies. Bitcoin, Ether etc. are public crypto built on public blockchains and have their own specific use cases," said Nischal Shetty, founder of WazirX.

"They are needed to run smart contract and write to the distributed ledger that theyre built on top of. People cannot use INR or USDT to pay for fees on the Bitcoin or Ethereum Blockchain," Nischal Shetty further added.

BuyUcoin CEO Shivam Thakral said he expects the Bill to accommodate the aspirations of Indian crypto owners, Indian crypto entrepreneurs, and investors who have put their faith in India's crypto growth story.

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Black Friday Sales Come Early for Cryptocurrency Investors: 2 Top Tokens That Just Went on Sale – Motley Fool

For investors looking for deals on top cryptocurrencies, this past week provided what may turn out to be an early Black Friday sale. The majority of large-cap cryptocurrencies have taken a hit of late, outside of specific groups of digital assets, such as those tied to the metaverse, that cryptocurrency investors have latched on to.

Unfortunately, the cryptocurrency world appears to be taking on some of the characteristics of other asset classes. Whether that's good or bad, macroeconomic factors now play into the valuations of these digital assets, perhaps more than ever.

Image source: Getty Images.

This week, one of the key catalysts that drove down valuations across most highly valued asset classes was the renomination of Jerome Powell as Federal Reserve Chairman. While the market appeared to initially view this renomination as positive, it has become clear that investors were pricing in some probability that a more dovish option would be chosen. This sell-off continued into Tuesday and Wednesday, with the Nasdaqand cryptocurrency markets under pressure.

Now, investors have certainly been rewarded with a "buy the dip" approach to risk assets over the past decade. For those looking to do just that, there are certainly some juicy discounts to jump on with top cryptocurrencies. Here are two great options to consider right now.

Currently the sixth-largest cryptocurrency by market capitalization,Cardano(CRYPTO:ADA) is one of the cryptocurrencies that's been under pressure of late. Since hitting an all-time high of $3.10 on Sept. 1, Cardano has lost more than 45% of its value.

Among the key reasons why investors like Cardano is this network's speed and scalability. Cardano can reportedly handle more than 250 transactions per second right now, compared to around 4.6 for Bitcoin(CRYPTO:BTC) and 15 to 20 for Ethereum(CRYPTO:ETH). These numbers are expected to rise over time, as the network continues to be updated. For a large-cap cryptocurrency network, Cardano is fast.

Additionally, Cardano's proof-of-stake protocol has been enticing for investors considering alternatives to Bitcoin and Ethereum. While Ethereum is moving toward the adoption of a proof-of-stake model, Cardano remains one of the largest proof-of-stake networks available to investors right now.

The recent declines we've seen in Cardano appear to be the result of two key factors.

First, the network has seen slower adoption among developers for decentralized finance (DeFi) apps. Cardano's recent Alonzo hard fork brought smart contract functionality to Cardano. Thus, this token got bid up earlier in August in advance of the Sept. 12 launch. However, a rather disappointing showing on this front has led to a corrective sell-off among investors.

Additionally, this week it was revealed that cryptocurrency exchange eToro will delist Cardano. Regulatory concerns were cited as the rationale for this decision, though few specifics were given. Accordingly, investors remain on edge with Cardano right now.

That said, for those taking the longer view on Cardano, these shorter-term headwinds could prove to be a great opportunity to buy it. As investors continue to look at proof-of-stake networks with smart contract capabilities and the potential for growth, there's a tangible thesis to own this top cryptocurrency right now -- especially at a generous discount to recent highs.

Tezos (CRYPTO:XTZ) is a cryptocurrency investors need to go a little further down the list to find. This is another token that has been beaten up by the market of late. Since hitting a high of $9.18 on Oct. 3, Tezos has lost approximately 45% of its value.

However, this cryptocurrency is one I remain bullish on, for various reasons.

Tezos is a leader in the security token space. By security tokens, I'm not referring to the security or integrity of the blockchain itself -- on that front, Tezos receives top marks, alongside most of the major digital assets on the market. Rather, Tezos' Layer 1 platform (a term referring to actual blockchains and their tokens) allows for the tokenization of securities that normally are traded off-blockchain. Think of the various financial products investors may buy on an exchange (stocks, bonds, etc.).

Essentially, Tezos provides functionality to allow for assets to be traded on the blockchain. By tokenizing various asset classes, investors can expand their range of investments on the blockchain in a secure and seamless fashion.

One of the attributes that make Tezos so enticing in the security token space is the fact that this blockchain is self-amending. Rather than using hard forks (such as the aforementioned Alonso hard fork Cardano recently implemented), Tezos' blockchain includes an on-chain mechanism to update, rather than requiring simultaneous updates from nodes on the network.

Unfortunately for investors in Tezos, it appears that heightened regulatory risks pertaining to the cryptocurrency sector continue to provide headwinds for networks engaging in tokenization. The Biden administration has recently moved to tax cryptocurrency more heavily. Various Securities and Exchange Commission investigations into whether several crypto-related assets are deemed "securities" under the law have plagued this sector for some time. And countries like China and India appear to remain inflexible with their stance on cryptocurrency right now.

However, those with a longer-term time horizon may want to think about a future where blockchain technology can truly make a difference in the world. In the DeFi space, Tezos provides a solid investment thesis as a leader in security tokens. This is one area I think could provide tremendous value in the years and decades to come. Accordingly, investors may want to keep their eye on these tokens at these discounted levels today.

This article represents the opinion of the writer, who may disagree with the official recommendation position of a Motley Fool premium advisory service. Were motley! Questioning an investing thesis -- even one of our own -- helps us all think critically about investing and make decisions that help us become smarter, happier, and richer.

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How AI Is Poised to Help Humanity – Entrepreneur

Opinions expressed by Entrepreneur contributors are their own.

Recent advances in artificial intelligence have caused a surge of public and business interest in this remarkable technology. Though most of its applications are still in their infancy, professionals across a wide range of industries have begun using AI-infused assistants to accomplish various tasks. This accelerated pace of innovation and data usage has, however, led to increased uncertainty about just where machine learning and thinking is headed, and the impacts it will have on society. In terms of positive effects, I certainly see some standouts.

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AI can consider multiple scenarios and make educated decisions using both previously gathered information as well as real-time data. As a result, the errors are reduced and the chance of reaching broader accuracy and greater precision is much greater. Example:The forecasting of weather and other natural disasters such as earthquakes and tsunamis.

Robots applying machine learning can risk dangers that might otherwise be lethal for humans. Think of exploring space or the deepest parts of oceans, defusing a bomb or inspecting unstable structures. Example:In the still-lethal areas of theChernobyl nuclear disaster site in Ukraine, robots are conducting radiation surveillance, removing debris from the destroyed reactor, taking samples of radiological materials and even burying radioactive materials.

Related: How AI Can Deliver Clean Water to Billions

Conducting a repetitive task is definitionally tedious, and often time-consuming. Using AI for monotonous and routine actions can help direct focus to other components of a to-do list and free us to be increasingly creative. Example:Apple's Siri, Google Assistant and Amazon's Alexa, along with a newer arrival, C9 Companion, are intelligent assistants that can carry a meaningful conversation and help manage and organize daily life (such as answering emails, texting friends and other tasks).

AI can make decisions and carry out actions far faster than humans, and latest generations of machine learning can consider facts and statistics as well as learn aspects of human emotion, then weigh both into its calculations.Example:In health care, AI can help doctors and researchers diagnose cancer more accurately and efficiently, making treatment more effective.

Using AI, we can make robots that can function all day, every day, without break. Plus, unlike we humans, they do not become bored or disinterested in repetitive tasks.Example:Chatbots for customer service or hotlines. Offering 24/7 customer service is essential for global companies as they have customers from all over the world who reside in different time zones. AI solutions are winnings ways of them staying connected with customers.

Related: 3 Ways Machine Learning Can Help Entrepreneurs

AI is powering inventions in a variety of sectors that will help humans solve complex problems, expanding our creativity and ingenuity in the process.Examples include medical devices, drug synthesizers, weapons even kitchen appliances. In part this is because machine learning can create unpredictable, innovative outcomes autonomously, rather than merely following instructions, and can do so without human bias.

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Congress must clarify how the infrastructure bill will impact cryptocurrency – TechCrunch

Christopher MortonContributor

The $1 trillion U.S. infrastructure bill, signed into law by President Joe Biden last week, contains provisions that would tax cryptocurrency trades and yield the U.S. government some $2.8 billion a year.

That is, frankly, not a lot of money.

The issue is that the crypto tax element of the law is not clearly written, and the government risks squashing a burgeoning part of the economy.

The infrastructure bill says a brokerage needs to keep track of these things. But you can enter into a smart contract without a brokerage, so who is responsible for reporting in that case? Would a miner be considered a brokerage?

Theres no question that, on some level, the government is due taxes earned from cryptocurrency trading like any other investment gain typically at the time a person liquidates, or like a transfer of property. But the vagueness of the law risks either trading platforms eliminating access for U.S. citizens or simply preventing smaller cryptocurrency investors from joining or remaining in the market.

Weve seen this before. FATCA, the Foreign Account Tax Compliance Act, caused some financial institutions to block U.S. citizens from using their services because the compliance rules were too burdensome relative to the risk and potential benefit.

Here are a few scenarios some simple and some complex that need to be thought through:

The minimum is $10,000 a carryover from the Bank Secrecy Act. Transactions below that amount are not taxed, but $10,000 is a fairly low amount of money to have to deal with a complex tax situation.

The tax reporting for trading platforms and investors may be onerous enough to discourage further investment, which ultimately may make the tax worthless, or at least generate far less revenue than estimated.

And for the IRS, this could be a complex tax to audit. They will need a way to tie identities to these transactions. This is already done on trading platforms like Coinbase, but individual miners typically do not.

Whats somewhat noteworthy about this particular bill is that while tax laws will almost always be problematic initially, they usually get clarified over time. This infrastructure bill seemed to go the opposite direction. Congress started with the impact number ($1.1 trillion) and then tried to find ways to generate enough taxes to match the number.

This is unusual in a few ways, but perhaps indicative of our current political climate. Politicians used to start with the specific programs they wanted to fund, then tried to make the cost as small as possible. This time, both parties were fighting to promise a larger number when their party was in power. (Trump, after all, worked on a $2 trillion infrastructure bill, though it was never signed into law.)

Its a bit of a strange time in the U.S. politically, with mayors from Miami to New York and across the political spectrum offering to take their paychecks in cryptocurrency. Meanwhile, on the national level, theres no clear guidance on the federal governments long-term plans.

Ultimately, cryptocurrency is here to stay in one form or another, and the federal government needs to get serious about an approach by talking to experts like economists, academics and cryptocurrency platform developers.

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Lore has it that there’s a lull leading up to Santa Claus rallies here’s what the statistics show – MarketWatch

The stock market does not suffer from seasonal weakness between now and Christmas.

You may have never heard about a supposed pre-Santa lull on Wall Street. Given the medias relentless focus on a so-called Santa Claus Rally, you probably have been focused instead on the possibility of stock market strength in the weeks leading up to Christmas.

But you should never underestimate analysts appetite for slicing and dicing the data in new ways. And one such slice of the historical data is suggesting that, because were in the first year of the presidential cycle, stocks will go sideways between now and Christmas, which is when year-end strength kicks in.

Dont believe it.

Theres no seasonally based reason to expect the stock markets performance leading up to Christmas to be any different than it is over any other five-week stretch of the calendar. That doesnt mean the stock market wont exhibit weakness in coming weeks. But if it does, it wont have anything to do with it being late November and the first weeks of December in the first year of the presidential cycle.

The accompanying chart, below, focuses on the Dow Jones Industrial Average DJIA, -0.03% back to its creation in 1896, measuring its average return between Nov. 17 and Dec. 24. Its average gain over this five-week period is 0.54%, versus an average of 0.72% for all five-week periods across the entire calendar. If I were to have stopped the analysis at this point, you would have some basis for thinking there is a pre-Santa lull though the difference of 18 basis points is of doubtful statistical significance.

But notice the results when segregated by year of the presidential cycle. The first year of that cycle this year, in other words has produced an average DJIA gain of 0.74% in this pre-Santa period. While the 2-basis-point margin above the overall average is not statistically significant, the more important takeaway is that there is no reason to expect this year will be a below-average one for the stock market.

Even if the stock markets performance over the five weeks prior to Christmas differed in a statistically significant way from the long-term average, however, the pattern would have to jump over another hurdle before it would make sense to bet on it. That hurdle is the need for a theoretical justification for why that pattern should exist in the first place.

I am aware of no such explanation in the case of a pre-Santa lull in the first year of the presidential cycle. And Im not holding my breath that any will ever be found.

Thats because this pattern, as well as most other seasonal patterns, are the result of shameless data-mining exercises. As any statistician can attest, upon torturing the data long and hard enough you can get it to say almost anything you want.

My favorite example of data mining comes from David Leinweber, head of the Center for Innovative Financial Technology at the Lawrence Berkeley National Laboratory. He found that you could explain 99% of the variation in the S&P 500s SPX, +0.23% return with a simple model containing just four inputs: Butter production in Bangladesh, American cheese production, and American and Bangladeshi sheep populations.

This isnt to say that no seasonal patterns exist. A few do, and they rest on strong theoretical foundations.

But most do not, so you should be skeptical whenever you read or hear of another analyst discovering some uncanny pattern in the stock market. Your first instinct should be to think back to Leinwebers example and how obviously irrelevant to the stock market are butter, cheese and sheep in Bangladesh and the U.S.

Mark Hulbert is a regular contributor to MarketWatch. His Hulbert Ratings tracks investment newsletters that pay a flat fee to be audited. He can be reached at mark@hulbertratings.com.

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Women Innovators And Researchers Who Made A Difference In AI In 2021 – Analytics India Magazine

There is a troubling and persistent absence of women when it comes to the field of artificial intelligence and data science. Women constitute a mere 22 per cent or less than a quarter of professionals in this field, as says the report Where are the women? Mapping the gender job gap in AI, from The Turing Institute. Yet, despite low participation and obstacles, women are breaking the silos and setting an example for players out in the field of AI.

To honour their commitment and work done, we have listed some of the women innovators and researchers who have worked tirelessly and contributed significantly to the field of AI and data science. The list below is provided in no particular order.

The brainchild behind and the founder of The Algorithmic Justice League (AJL), Joy Buolamwini, has started the organisation that combines art and research to illuminate the social implications and harms of artificial intelligence. With her pioneering work on algorithmic bias, Joy opened the eyes of the world and brought out the gender bias and racial prejudices embedded in facial recognition systems. As a result, Amazon, Microsoft, and IBM all halted their facial recognition services, admitting that the technology was not yet ready for widespread usage. One can watch the famous documentary Coded Bias to understand her work. Her contributions will surely pave the way for a more inclusive and diversified AI community in the near future.

A large chunk of researchers and scholars focus on improving algorithms for the machines to work efficiently. However, Cynthia Rudin, the Duke University computer science professor and engineer, worked tirelessly to utilise the power of AI to serve humanity and help society. As a result, she was bestowed with the Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity 2021. Her research work majorly focuses on machine learning tools that help humans make better decisions, mainly interpretable ML and its applications. In a conversation with us, Cynthia wished that AI could solve the refugee crisis, reverse climate change and help end extreme poverty.

Allie is currently the Global Head of Machine Learning Business Development, Startups and Venture Capital at Amazon Web Services. She is in the supporting role for many big AI organisations. In her effort to increase representation, Allie co-founded Girls of the Future, an organisation that showcases girls aged between 13 to 18 who are innovating in the field of STEM. Moreover, she will be presenting the Data and Machine Learning keynote at AWS re: Invent with Swami Sivasubramanian, VP, Amazon AI. She formerly worked at IBM, where she oversaw large-scale product development using computer vision, conversation, data, and regulation.

Dr Lucia is a Natural Language Processing Professor at the Imperial College of London. She built and led the Language and Multimodal AI Lab, managing a team of 20 researchers and her research examines many aspects of data-driven approaches to language processing, with a focus on multimodal and multilingual context models. Her work has benefited several fields, including machine translation, quality estimate, image captioning, and text adaptation. She is currently involved and working on a number of machine translation projects, including multilingual video captioning and text adaptation, which will surely contribute to the advancement of AI-driven technologies in this field.

A prominent advocate of safe and reliable AI, Cassie is currently the Chief Decision Scientist at Google. Recently, she introduced the AI-for-everyone course Making Friends with Machine Learning. Her research interests include applied artificial intelligence and data science process architecture. At Google, Cassie co-founded the field of decision intelligence at Google, combining social science, decision theory, and managerial science with data science to better understand how actions lead to results.

Although females are under-represented in the field of AI and technology, all is not lost yet; some incredibly inspirational female pioneers are shaping the world of AI.

Kumar Gandharv, PGD in English Journalism (IIMC, Delhi), is setting out on a journey as a tech Journalist at AIM. A keen observer of National and IR-related news. He loves to hit the gym. Contact: [emailprotected]

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Who Says AI Is Not For Women? Here Are 6 Women Leading AI Field In India – SheThePeople

I dont see tech or AI as hostile to women. There are many successful women in AI both at the academic as well as industry levels, says Ramya Joseph, the founder of AI-based entrepreneurial start-up Pefin, the worlds first AI financial advisor. And even on my team at Pefin, women hold senior technology positions. There tends to be a misconception that tech tends to attract a geeky or techy kind of personality, which is not the case at all,

Joseph has a bachelors degree in computer science and masters in Artificial Intelligence, Machine Learning and Financial Engineering. As a wife, mother and daughter, Joseph could closely relate to the crisis of financial advice to plan for the future. She came up with the idea of founding Pefin when her father lost his job due to a lack of financial advice when he jeopardised his retirement plans. Navigating and solving his problems, Joseph realised that many were telling the same problem. Hence she came up with the idea of an AI-driven financial adviser.

No doubt Artificial Intelligence is one of the growing industries in the field of professionalism. As new inventions and developments knock at our doors, the relation between humans and computers is being reassessed. With the expansion of AI, new skills and exceptional human labour is in high demand. But the problem is that despite the evolution in society, the gender pay gap is not shrinking. As per the wef forum, only 22 per cent of AI professionals are women. The report suggests that there is a gender gap of around 72 per cent.

Despite this, many women are breaking the glass ceilings and reforming the field of Artificial Intelligence. Through their skills and leadership, these women are carving the path for other women to participate as AI professionals. So in this article, I am going to list out some women AI professionals in India who changing the gender dynamics through their excellence.

Amarjeet Kaur is a research scientist at TechMahindra. She has a PhD in Computer Science and Technology. Kaur specialises in research techniques and technologies like graph-based text analysis, latent semantic analysis and concept maps among others. She also has expertise in experimentation and field research, data collection and analysis and project management. She is known for her organisational skills and willingness to take charge.

Kaur has also worked with the Department of Science and Technology at Women Scientist Scheme. As a part of the scheme, she helped in developing a technique to automatically evaluate long descriptive answers. With more than ten years of research and teaching experience, Kaur has excellent academic skills. Her academic skills and innovative techniques have gained her a gold medal and a toppers position at Mumbai University. Her innovative skills and course material has also received a place in Mumbai Universitys artificial intelligence and machine learning courses.

Sanghamitra Bandyopadhyay works at the Machine Intelligence Unit of the Indian Statistical Institute. She also completed her PhD from the institute and became its director serving for the years 2015 to 2020. Bandyopadhyaya is also a member of the Science, Technology and Innovation Advisory Council of the Prime Minister of India (PM-STIAC). She specialises in fields like machine learning, bioinformatics, data mining and soft and evolutionary computation.

She has been felicitated with several awards for her work like Bhatnagar Prize, Infosys award, TWAS Prize, DBT National Women Bioscientist Award (Young) and more. She has written around 300 research papers and has edited three books.

Ashwini Ashokan is the founder of MadStreetDen, an artificial intelligence company that uses image recognising platforms to power retail, education, health, media and more. Starting up in 2014, the venture is headquartered in California with offices access Chennai, Bangalore, Tokyo, London and more. She co-founded the platform along with her husband. Speaking to SheThePeople, Ashokan said, Its only natural that the AI we build mimics what weve fed it, until the agency of its own, which could be good or bad. As an industry, we need to think about what were teaching our AI, She also added, Every line of code we write, every feature we put in products we need to ask ourselves, what effect does this have on the way the world will be interacting with it.

Apurva Madiraju is a vice president at Swiss Re Global Business Solutions India in Bangalore. She is leading the data analytics and data science team of the audit function. As the leader, she is responsible for building machine learning and text analytics solution to deal with audit compliance risk.

Madiraju flaunts 11 years of experience across diverse fields like artificial intelligence, data science, machine learning and data engineering. She has developed multiple AI and ML-driven solutions like ticket volume forecasting models, turn-around-time prediction solutions and more. She has worked across companies globally to lead the conceptualisation, development and deployment of many AI and ML-based solutions for enterprises.

With more than 20 years of experience as a Data Scientist, Bindu Narayan serves as the Senior Manager with Advanced Analytics and AI at EY GDS Data and Analytics Practice. At EY, Narayan is AI competency leader for EYs Global Delivery Services. She along with her team offers virtual assistant solutions to clients across the industry. Moreover, with her skills, Narayan has developed many innovative AI solutions and leads in the field of machine learning, customer and marketing analytics and predictive modelling. She completed her PhD from IIT Madras on the topic of modelling Customer Satisfaction and Loyalty.

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Is Bitcoin the Only Cryptocurrency You Need? – Motley Fool

The cryptocurrency world is now awash with new altcoins, with Shiba Inu (CRYPTO:SHIB) being the latest to receive a huge surge of appeal among retail traders. However, that craze could be fizzling out.

As more new coins come out, the huge moves of shiny new altcoins can be tantalizing. But unless you are really involved deeply in the crypto world and know a lot about the technology and potential real-world use cases, I'd be wary investing in any cryptocurrency long-term other than Bitcoin (CRYPTO:BTC)... and recent events have only seemed to cement Bitcoin's lead.

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While a lot the investing community views bitcoin as a potential store of value and payment network, rival coin Ethereum (CRYPTO:ETH) was designed more specifically for decentralized finance (DeFi) applications, such as smart contracts. In fact, Shiba Inu is a token that's built on Ethereum's blockchain. Ethereum's status as a go-to platform for DeFi applications is what gives it much of its value.

However, a new Bitcoin update called Taproot now gives Bitcoin enhanced DeFi capabilities too. Just activated this month, Taproot allows multiple cryptographic signatures to be recorded as a single signature on the Bitcoin blockchain. While it may sound like small change, the new innovation will greatly enhance the ability to record smart contracts on on top of the Bitcoin blockchain. Taproot also enables greater privacy for those transactions, allowing transacting parties to only submit crucial information to the blockchain without revealing all identifiers.

That essentially means Bitcoin can now be used for DeFi applications, plugging a current hole in its functionality and making it more competitive with Ethereum.

Don't think Taproot is a big deal? Twitter (NYSE:TWTR) and Square (NYSE:SQ) CEO Jack Dorsey does. He just released a white paper for a new Bitcoin-based DeFi application on November 19, shortly after the Taproot update was activated. The paper outlines plans for a decentralized Bitcoin exchange at Square called tbDEX. While the paper has been hinted at for a long time, it was only released after Taproot was activated.

Jack Dorsey is a bitcoin enthusiast and believes Bitcoin will become the "native currency of the internet." There aren't two internets, so do there need to be two cryptocurrencies that can be used throughout the world? And as a general currency inflation hedge, speculators have traditionally speculated on gold, perhaps some silver -- but not may other commodities that have their own supply demand dynamics.

Famed hedge fund manager Bill Miller also agrees Bitcoin's first-mover status and wide buy-in from large financial institutions gives it a lead that's tough to surmount. As is the case with many internet companies, once a network achieves escape velocity in terms of acceptance, network effects generally make it a winner-take-all or winner-take-most situation. In other words, once bitcoin has been institutionalized and widely accepted as a viable asset class, another digital asset would have to be decidedly better than Bitcoin in order to displace it.

Dorsey and Miller are joined in their Bitcoin-only outlook by MicroStrategy (NASDAQ:MSTR) CEO Michael Saylor. Saylor has been buying Bitcoin with MicroStrategy's corporate cash as fast as he can -- a risky strategy that essentially turns the software company into a leveraged Bitcoin fund, with a nice little software business on the side.

In a recent interview, Saylor expressed his own prediction that the world's sovereign currencies could consolidate, much like all the currencies of Europe consolidated into the Euro. And if there are only a few regional government-backed currencies for the world, there may very well only be room for one universally agreed-upon digital alternative, which is likely Bitcoin.

Going back to Miller, he said in a recent interview, "There are 10,000 various tokens and stuff floating out there. The chances of more than a handful of them being worthwhile is very, very small. Bitcoin, Ethereum, and a few others are probably going to be around for a while."

Of course, since that interview, Taproot was activated, perhaps neutralizing Ethereum's biggest strength. So Miller's list of winning cryptocurrencies may be even smaller -- leaving Bitcoin as perhaps the last cryptocurrency standing.

For new investors interested in crypto, I still think it's important to make Bitcoin the largest position in any crypto basket -- especially those with a longer-term outlook.

This article represents the opinion of the writer, who may disagree with the official recommendation position of a Motley Fool premium advisory service. Were motley! Questioning an investing thesis -- even one of our own -- helps us all think critically about investing and make decisions that help us become smarter, happier, and richer.

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Is Bitcoin the Only Cryptocurrency You Need? - Motley Fool

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Machine learning optimization of an electronic health record audit for heart failure in primary care – DocWire News

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ESC Heart Fail. 2021 Nov 23. doi: 10.1002/ehf2.13724. Online ahead of print.

ABSTRACT

AIMS: The diagnosis of heart failure (HF) is an important problem in primary care. We previously demonstrated a 74% increase in registered HF diagnoses in primary care electronic health records (EHRs) following an extended audit procedure. What remains unclear is the accuracy of registered HF pre-audit and which EHR variables are most important in the extended audit strategy. This study aims to describe the diagnostic HF classification sequence at different stages, assess general practitioner (GP) HF misclassification, and test the predictive performance of an optimized audit.

METHODS AND RESULTS: This is a secondary analysis of the OSCAR-HF study, a prospective observational trial including 51 participating GPs. OSCAR used an extended audit based on typical HF risk factors, signs, symptoms, and medications in GPs EHR. This resulted in a list of possible HF patients, which participating GPs had to classify as HF or non-HF. We compared registered HF diagnoses before and after GPs assessment. For our analysis of audit performance, we used GPs assessment of HF as primary outcome and audit queries as dichotomous predictor variables for a gradient boosted machine (GBM) decision tree algorithm and logistic regression model. Of the 18 011 patients eligible for the audit intervention, 4678 (26.0%) were identified as possible HF patients and submitted for GPs assessment in the audit stage. There were 310 patients with registered HF before GP assessment, of whom 146 (47.1%) were judged not to have HF by their GP (over-registration). There were 538 patients with registered HF after GP assessment, of whom 374 (69.5%) did not have registered HF before GP assessment (under-registration). The GBM and logistic regression model had a comparable predictive performance (area under the curve of 0.70 [95% confidence interval 0.65-0.77] and 0.69 [95% confidence interval 0.64-0.75], respectively). This was not significantly impacted by reducing the set of predictor variables to the 10 most important variables identified in the GBM model (free-text and coded cardiomyopathy, ischaemic heart disease and atrial fibrillation, digoxin, mineralocorticoid receptor antagonists, and combinations of renin-angiotensin system inhibitors and beta-blockers with diuretics). This optimized query set was enough to identify 86% (n = 461/538) of GPs self-assessed HF population with a 33% reduction (n = 1537/4678) in screening caseload.

CONCLUSIONS: Diagnostic coding of HF in primary care health records is inaccurate with a high degree of under-registration and over-registration. An optimized query set enabled identification of more than 80% of GPs self-assessed HF population.

PMID:34816632 | DOI:10.1002/ehf2.13724

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Machine learning optimization of an electronic health record audit for heart failure in primary care - DocWire News

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