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The ABCs of AI, algorithms and machine learning (re-air) – Marketplace

This episode originally aired on July 20, 2022.

Advanced computer programs influence, and can even dictate, meaningful parts of our lives. Think streaming services, credit scores, facial recognition software.

And as this technology becomes more sophisticated and more pervasive, its important to understand some basic terminology.

On this Labor Day, were revisiting an episode in which we explore the terms algorithm, machine learning and artificial intelligence. Theres overlap, but theyre not the same things.

We called up a few experts to help us get a firm grasp on these concepts, starting with a basic definition of algorithm. The following is an edited transcript of the episode.

Melanie Mitchell, Davis professor of complexity at the Santa Fe Institute, offered a simple explanation of a computer algorithm.

An algorithm is a set of steps for solving a problem or accomplishing a goal, she said.

The next step up is machine learning, which uses algorithms.

Rather than a person programming in the rules, the system itself has learned, Mitchell said.

For example, speech recognition software, which uses data to learn which sounds combine to become words and sentences. And this kind of machine learning is a key component of artificial intelligence.

Artificial intelligence is basically capabilities of computers to mimic human cognitive functions, said Anjana Susarla, who teaches responsible AI at Michigan State Universitys Broad College of Business.

She said we should think of AI as an umbrella term.

AI is much more broader, all-encompassing, compared to only machine learning or algorithms, Susarla said.

Thats why you might hear AI as a loose description for a range of things that show some level of intelligence. Like software that examines the photos on your phone to sort out the ones with cats to advanced spelunking robots that explore caves.

Heres another way to think of the differences among these tools: cooking.

Bethany Edmunds, professor and director of computing programs at Northeastern University, compares it to cooking.

She says an algorithm is basically a recipe step-by-step instructions on how to prepare something to solve the problem of being hungry.

If you took the machine learning approach, you would show a computer the ingredients you have and what you want for the end result. Lets say, a cake.

So maybe it would take every combination of every type of food and put them all together to try and replicate the cake that was provided for it, she said.

AI would turn the whole problem of being hungry over to the computer program, determining or even buying ingredients, choosing a recipe or creating a new one. Just like a human would.

So why do these distinctions matter? Well, for one thing, these tools sometimes produce biased outcomes.

Its really important to be able to articulate what those concerns are, Edmunds said. So that you can really dissect where the problem is and how we go about solving it.

Because algorithms, machine learning and AI are pretty much baked into our lives at this point.

Columbia Universitys engineering school has a further explanation of artificial intelligence and machine learning, and it lists other tools besides machine learning that can be part of AI. Like deep learning, neural networks, computer vision and natural language processing.

Over at the Massachusetts Institute of Technology, they point out that machine learning and AI are often used interchangeably because these days, most AI includes some amount of machine learning. A piece from MITs Sloan School of Management also gets into the different subcategories of machine learning. Supervised, unsupervised and reinforcement, like trial and error with kind of digital rewards. For example, teaching an autonomous vehicle to drive by letting the system know when it made the right decision not hitting a pedestrian, for instance.

That piece also points to a 2020 survey from Deloitte, which found that 67% of companies were already using machine learning, and 97% were planning to in the future.

IBM has a helpful graphic to explain the relationship among AI, machine learning, neural networks and deep learning, presenting them as Russian nesting dolls with the broad category of AI as the biggest one.

And finally, with so many businesses using these tools, the Federal Trade Commission has a blog laying out some of the consumer risks associated with AI and the agencys expectations of how companies should deploy it.

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Five Experts Address Trends in Artificial Intelligence and Machine Learning – PR Newswire UK

Trends and risks in a Golden Age of Artificial Intelligence and Machine Learning

By David Churbuck, Founder & former Editor-in chief of Forbes.com

NEW YORK, Sept. 5, 2022 /PRNewswire/ -- Over the summer of 2022, Wow AI, a global provider of high-quality AI training data based in New York City, invited a panel of experts from different industries and areas of expertise to share their insights into the current state of artificial intelligence and machine learning (AI/ML) and discuss the factors that have accelerated the recent adoption of AI in applications.

All the experts agreed that the past decade has been a Golden Age for AI, made possible by the affordable availability of AI services delivered from the cloud and the inexpensive power of graphics processing units designed to handle the types of transforms and calculations at the foundation of AI models.

However, they each have a unique perspective on different trends and issues as AI pervades society, continuously improving the human-machine interface and becoming more embedded in every aspect of our lives.

The Experts

The five experts will share more insights along with more than 20 other thought leaders in AI/ML recruited from Fortune 500 companies and organizations around the world such as Walt Disney, Deloitte, Microsoft, Oxford Brookes University, The US Department of Commerce and many others, during a two-day online discussion of contemporary AI and ML trends on September 29-30 hosted by Wow AI.

Welcome and thanks for joining.There are fears about an AI going out of control such as Skynet or Hal 9000 or devices like Amazon Alexa or Google Assistant or Apple Siri. 75 years later, there is legislation pending at the state and federal levels to regulate AI and review algorithms for signs of bias or the perpetuation of old models that could deny a person equal opportunity. What is the risk the gains of the past ten years could be reversed or future developments hindered by fear, baseless conspiracy theories, or over-regulation?

Andreas: I think if we look at people and humanity as a whole, there has always been a fear of not being the pinnacle of evolution. You need to make sure that the people who are affected by the change are part of the process, that they are aware of why and how you want to introduce a piece of technology like AI, what the limitations are, and where it can help them become better and more effective.

Noelle: There are more threatening devices than Alexa. The average smartphone has 50 applications trying to get permission to access our camera, microphone, and contacts. I've consistently been opposed to AI being applied to anything demographically oriented. [...] Biases end up perpetuating bad behavior. Maybe the models need to be infused with some inclusivity."

In the 1980s AI seemed to have potential in decision support systems but then it seemed to stall. Then, almost overnight it was in our cars, our phones, and our living rooms, to the point where we're looking at autonomous vehicles, real-time meeting transcription, and in the case of Aravind's company, Uniphore, analyzing customer interactions for tone and emotion. What happened that helped AI get over the hype that surrounded it in the past while delivering significant results after so many years of being ignored?

David Von Dollen: I would say two factors brought AI out of its "winter". One was hardware computing power primarily in the form of GPUs which have had a tremendous impact. The other factor is ongoing refinements to the underlying algorithms.

Patrick Bangert: This renaissance of AI we are experiencing today is sometimes called the "Deep Learning Revolution." Yes, some of it comes down to processing speed and we have the graphics processing units we didn't have 30 years ago, but it's not just about speed. Speed is mainly interesting and beneficial in the sense that it allows us to train much bigger models in the same amount of time. The second benefit is scientific. A lot of headway is being realized in deep learning due to the mathematics of AI gaining novel algorithms and modeling methods that are better than what we had in the 1980s.

Aravind Ganapathiraju: The difference is accuracy. The first ASR system (automated speed recognition) had a 40% error rate. On the same task today we are pushing a 5% error rate. I'm not saying it's a solved problem, but it is indicative of the evolution that has happened over the last two decades.

Let's talk about the role data has played in helping AI/ML deliver on its promises. Aside from strict laws governing the processing and storage of personal data and regulations to ensure data privacy, what should providers of AI-enabled products and services be thinking about when it comes to data?

Aravind: One of the latest products we have released at Uniphone is "Q for Sales" which analyzes conversations not just by examining the tonal information in a call such as this one, but also by other visual cues. The fusion of different data types from audio to text, from video to facial expressions provides a call center person with valuable insights and nudges to gain a better outcome from the call.

Andreas Welsch: A byproduct of the early 2000s' Big Data trends has been an influx of so many data points that it's not possible for one individual or even a team of five or ten data scientists to analyze at the speed, scale, and quality needed to make decisions in business today. With the application of AI on the task, we're able to detect these patterns in the data that allow you to automate certain parts of your business processes in a way that has never been possible before.

I also think, to Aravind's point, that there are just so many more data pools available and now we have the tools to analyze them on a much larger scale than ever before.

Patrick Bangert: At Samsung, we train all sorts of models. [...] The role data plays across the company is driving AI systems to forecast how many people will buy a particular Samsung device, at which stores, and how to get inventory to those stores upon launch. Our internal data is the fuel for those forecasting systems, data unique to our business and our success.

Noelle Silver: I think Web3 is really forcing people to rethink data and there's an ethical shift between the collectors of data and the sources of that data. Companies like Apple distinguish themselves by popping up a little dialogue that asks "Do you want to share your real address or would you like us to mask it for you?" That translates in my mind to more responsibility on the part of the companies to be responsible, ethical stewards of their users' data.

David Von Dollen: I focus a lot on what I call "narrow AI" an algorithm that's trained on a specific set of data to perform a specific task. That's what a lot of our applications do today. It's all pretty much pattern recognition but within narrowly defined constraints. I think those types of applications may turn out to be much more harmful than some sentient AI taking over in a Skynet situation.

To watch the full conversation with the experts who will be keynoting the Worldwide AI Webinar, please visit:

These five thought leaders and other experts from around the world will be taking your questions, and discussing the issues and opportunities in AI and ML applications, training models, and data sources, and other topics at Worldwide AI Webinar on September 29 and 30th.

Media Contact: David Churbuck- Founder & former editor-in-chief of Forbes, a prize-winning tech journalist - David@churbuck.com

Photo -https://mma.prnewswire.com/media/1889768/2__1.jpg

SOURCE Wow AI

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How avatars and machine learning are helping this company to fast track digital transformation – ZDNet

Image: LNER

Digital transformation is all about delivering change, so how do you do that in an industry that's traditionally associated with largescale infrastructures and embedded operational processes?

Danny Gonzalez, chief digital and innovation officer (CDIO) at London North Eastern Railway (LNER), says the answer is to place technology at the heart of everything your business does.

"We firmly believe that digital is absolutely crucial," he says. "We must deliver the experiences that meet or exceed customers' expectations."

Delivering to that agenda is no easy task. Gonzalez says the rail journey is "absolutely full" of elements that can go wrong for a passenger, from buying a ticket, to getting to the train station, to experiencing delays on-board, and onto struggling to get away from the station when they reach their destination.

SEE: Digital transformation: Trends and insights for success

LNER aims to fix pain points across customer journeys, but it must make those changes in a sector where legacy systems and processes still proliferate. Gonzalez says some of the technology being used is often more than 30 years' old.

"There's still an incredible amount of paper and spreadsheets being used across vast parts of the rail industry," he says.

"Our work is about looking at how things like machine learning, automation and integrated systems can really transform what we do and what customers receive."

Gonzalez says that work involves a focus on the ways technology can be used to improve how the business operates and delivers services to its customers.

This manifests as an in-depth blueprint for digital transformation, which Gonzalez refers to as LNER's North Star: "That gives everyone a focus on the important things to do."

As CDIO, he's created a 38-strong digital directorate of skilled specialists that step out of traditional railways processes and governance and into innovation and the generation of creative solutions to intractable challenges.

"It's quite unusual for a railway company to give more permission for people to try things and fail," he says.

Since 2020, the digital directorate in combination with its ecosystem of enterprise and startup partners has launched more than 60 tools and trialled 15 proof-of-concepts.

One of these concepts is an in-station avatar that has been developed alongside German national railway company Deutsche Bahn AG.

LNER ran a trial in Newcastle that allowed customers to interact in free-flowing conversations with an avatar at a dedicated booth at the station. The avatar plugged into LNER's booking engine, so customers could receive up-to-date information on service availability. Following the successful trial, LNER is now looking to procure a final solution for wider rollout.

The company is also working on what Gonzalez refers to as a "door-to-door" mobility-as-a-service application, which will keep customers up to date on the travel situation and provide hooks into other providers, such as taxi firms or car- and bike-hire specialists.

"It's about making sure the whole journey is seamlessly integrated," he says. "As a customer, you feel in control and you know we're making sure that if anything is going wrong through the process that we're putting it right."

When it comes to behind-the-scenes operational activities, LNER is investing heavily in machine-learning technology. Gonzalez's team has run a couple of impactful concepts that are now moving into production.

SEE:What is digital transformation? Everything you need to know about how technology is reshaping business

One of these is a technology called Quantum, which processes huge amounts of historical data and helps LNER's employees to reroute train services in the event of a disruption and to minimise the impact on customers.

"Quantum uses machine learning to learn the lessons of the past. It looks at the decisions that have been made historically and the impact they have made on the train service," he says.

Gonzalez: "We firmly believe that digital is absolutely crucial."

"It computes hundreds of thousands of potential eventualities of what might happen when certain decisions are made. It's completely transforming the way that our service delivery teams manage trains when there's disruption to services."

To identify and exploit new technologies, Gonzalez's team embracesconsultant McKinsey's three horizon model, delivering transformation across three key areas that allows LNER to assess potential opportunities for growth without neglecting performance in the present.

Horizon one focuses on "big, meaty products" that are essential to everyday operations, such as booking and reservations systems, while horizon two encompasses emerging opportunities that are currently being scoped out by the business.

Gonzalez says a lot of his team's activity is now focused on horizon three, which McKinsey suggests includes creative ideas for long-term profitable growth.

He says that process involves giving teams quite a lot of freedom to get on and try stuff, run proof of concepts, and actually understand where the technology works.

Crucial to this work isan accelerator called FutureLabs, where LNER works with the startup community to see if they can help push digital transformation in new and exciting directions.

"We go out with key problem statements across the business and ask the innovators to come and help us solve our challenges and that's led to some of the most impactful things that we've done as a business," says Gonzalez.

FutureLabs has already produced pioneering results. Both the Quantum machine-learning tool and the "door-to-door" mobility service have been developed alongside startup partners JNCTION and IOMOB respectively.

LNER continues to search for new inspiration and has just run the third cohort of its accelerator. Selected startups receive mentoring and funding opportunities to develop and scale up technology solutions.

Gonzalez says this targeted approach brings structure to LNER's interactions and investments in the startup community and that brings a competitive advantage.

"It's not like where I've seen in other places, where innovation initiatives tend to involve 'spray and pray'," he says. "The startups we work with are clear on the problems they're trying to solve, which leads to a much greater success rate."

SEE: Four ways to get noticed in the changing world of work

Gonzalez's advises other professionals to be crystal clear on the problems they're trying to solve through digital transformation.

"Know what the priorities are and bring the business along with you. Its really important the business understands the opportunities digital can bring in terms of how you work as an organisation," he says.

"We're fortunate that we've got a board that understood that rail wasn't where it needed to be in terms of its digital proposition. But we've put a lot of work into creating an understanding of where issues existed and the solutions that we needed if we're going to compete in the future."

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Canadian company uses machine learning to promote DEI in the hiring process – IT World Canada

Toronto-based software company, Knockri has developed an AI-powered interview assessment tool to help companies reduce bias and bolster diversity, equity and inclusion (DEI) in the job hiring process.

Knockris interview assessment tool uses Natural Language Processing (NLP) to evaluate only the transcript of an interview, overlooking non-verbal cues, including facial expressions, body language or audio tonality. In addition, race, gender, age, ethnicity, accent, appearance, or sexual preference, reportedly, do not impact the interviewees score.

To achieve objective scoring, Faisal Ahmed, co-founder and chief technical officer (CTO) of Knockri, says that the company adopts a holistic and strategic approach in training their model, including constantly trying new and different data, training, and tests, that covers a wide range of representation in terms of race, ethnicity, gender, and accent, as well as job roles and choices. After training the model, the company conducts quality checks and adverse impacts analysis to analyze scoring patterns and ensure quality candidates do not fall through the cracks.

Though working with clients with high volume hiring such as IBM, Novartis, Deloitte, and the Canadian Department of National Defence, Ahmed says their model is not able to analyze for every job in the world. Once we have new customers, new geographies, new job roles or even new experience levels that were working with, we will wait to get an update on that, benchmark, retrain, and then push scores. Were very transparent about this with our customers.

To ensure that the data fed into the AI is not itself biased, Ahmed adds that the company avoids using data from past hiring practices, such as looking at resumes or successful hires from ten years ago, as they may have been recruiting using biased or discriminatory practices. Instead, Ahmed says, the AI model is driven by Industrial and Organizational (IO) psychology to focus purely on identifying the kind of behaviors or work activities needed for specific jobs. For example, if a customer service role requires empathy, the model will identify behaviors from the candidates past experiences and words that reflect that specific trait, Ahmed says.

He recommends that customers use Knockri at the beginning of the interview process when there is a reasonably high volume of applications, and the same experience, scoring criteria, and opportunities can be deployed for all candidates.

Ahmed says their technology seeks to help businesses lay a foundation for a fair and equitable assessment of candidates, and is not meant to replace a human interviewer. Decisions made by Knockri are reviewed by a human being, and later stages of the interview process will inevitably involve human interviewers.

Were not going to solve all your problems, but were going to set you on the right path, concludes Ahmed.

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Machine learning hiring levels in the retail banking industry rose in August 2022 – Retail Banker International

The proportion of major banks, central banks, and non-bank competitors companies hiring for machine learning related positions kept relatively steady in August 2022 compared with the equivalent month last year, with 35.1% of the companies included in our analysis recruiting for at least one such position.

This latest figure was higher than the 34.1% of companies who were hiring for machine learning related jobs a year ago and an increase compared to the figure of 34.1% in July 2022.

When it came to the rate of all job openings that were linked to machine learning, related job postings rose in August 2022 from July 2022, with 1.7% of newly posted job advertisements being linked to the topic.

This latest figure was the same as the 1.7% of newly advertised jobs that were linked to machine learning in the equivalent month a year ago.

Machine learning is one of the topics that GlobalData, from whom our data for this article is taken, have identified as being a key disruptive force facing companies in the coming years. Companies that excel and invest in these areas now are thought to be better prepared for the future business landscape and better equipped to survive unforeseen challenges.

Our analysis of the data shows that major banks, central banks, and non-bank competitors companies are currently hiring for machine learning jobs at a rate higher than the average for all companies within GlobalData's job analytics database. The average among all companies stood at 1% in August 2022.

GlobalData's job analytics database tracks the daily hiring patterns of thousands of companies across the world, drawing in jobs as they're posted and tagging them with additional layers of data on everything from the seniority of each position to whether a job is linked to wider industry trends.

You can keep track of the latest data from this database as it emerges by visiting our live dashboard here.

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Cyber-attacks in future will be about machine learning and automation around attacking and discovery of vulner – Times Now

Since the pandemic there has been a race to go digital, organisations and individuals alike. Remote working, remote learning, contactless payments, and online shopping has become the mainstay of our lives. While technology really helped us get through the pandemic, it came with a learning curve, the lack of awareness about dos and donts made many easy targets for hackers and hacking organisations. The rush to go digital exposed a lot of companies to cyber-attacks through ransomware, data breaches, and leaks. The Pegasus and Predator scandals also showed us that hacking now is a business and Politicians are also using the services of these companies for their own benefit. As technology moves ahead, the risk of cyber-attacks increases as well, to explain a bit more about the current cyber security scenario Siddharth Shankar from TimesNow speaks to Asaf Hecht, from CyberArk. Asaf manages one of the research groups in CyberArk Labs. He focuses on researching and discovering the latest attack techniques and applying lessons learned to improve cyber defenses. Prior to CyberArk, Asaf served for eight years in the Israeli Army, as a skilled helicopter pilot and as Team Leader for the advanced cyber-hunting team, an elite force that protects military top-secret networks and reveals APTs.

Excerpts

Asaf: You are absolutely correct about the situation during COVID, We saw it in the entire world that people and organization go on digital and work from home like me today, and everything is going for the internet and there is also a paradigm shift. The legacy of the traditional perimeter is finished. When you have a company and you are in a physical building, you can secure this building with what is going out and where is the entrance and exit.

Nowadays, everything is everywhere, and this is the need of the hour. The trends that you mentioned are correct. Also, contactless payment is also gaining popularity in countries like Israel. From bigger organizations to private ones to even individuals. From the youngest who thrive to use new technology to older people that don't have other options because the bank or store in the neighborhood was closed because it converted online. However, there are a few things that we need to remember regarding the outlook toward risk, it's different from the organisation side and different from the individual side of things.

For individuals, I think awareness is important to everyone and every age. Having said that, I think there are some principles that are very important.

For example, check your bank account and your credit card payment at least once per month. This is because there is a high chance of these kinds of things occurring without your knowledge. If you get scammed and phished and someone stole your credit card and pays for his desire. The most important thing for you is to detect it and then you can automatically alert the credit card company and also you will get a full refund from credit card company. The first principle, again simple awareness. The second principle is to check what goes out from your accounts and submit it for refund if something is wrong.

The third principle, the normal public is not the target, this is a big difference between attacks on organisations and attacks on individuals. While I believe threat actors can attack everyone, everything, and every device, it is only a matter of how much time is consumed, the budget and what are they gaining out of it. I can imagine myself if I have a phone and I don't think someone will want access to my phone, and it will offer him $10,000. I am sure it's not. There is nothing important there.

Siddharth - Phishing and getting money out of people or crypto wallets are quite common, but lately, we have been seeing a lot of state-sponsored threat actors and we have also been seeing private companies which are actually working only on finding exploits, finding zero-day hacks and then selling it to governments and making a big chunk of money. So now this is probably becoming a business as well and a lot of them actually stem from Israel. What are your thoughts on this? Is this quickly becoming a business prospect for hackers?

Asaf - I think it's an interesting trend and I agree with it. In the recent decade, there are few private companies that are mentioning that they do is to develop technology solutions for gaining access and intelligence. I think that in Israeli Cyber intelligence, getting cyber intelligence or getting cyber access to a device or target, has been around for over two decades, and then what happened is the people who were in the army service, some of them completed their service and they still wanted to do what they did. Also, we need to remember that most of the usage of these technologies is for humanitarian reasons for anti-terror fighting and for making sure there is no major terror attack. The fact is that for 20 years, we didn't have a devastating terror attack again (in Israel). And I think a major thing that helped this fact apart from other countermeasures is their demonstration is this kind of spying technologies that sometimes also comes from the private sector.

The challenge and the problem emanate from how you make sure that these kinds of technologies are being handled and sold to the right target. This is a problem, but these are the two sides of things. The world I think needed this kind of spying company to make it a safer place, but the problem from the other side is how to monitor and who these companies sell to and even if they sell it to a government, maybe the government will say, yeah. We are going to use it on a valid target, but I think these private companies don't really can audit, the usage of this third-party government.

Siddharth - Back in 2011, Bill Gates had said that the next big challenge for the world will not be a nuclear war, it would be a virus. We had COVID and the whole world just stopped. Do you think in the future, a full blown war like the Russia-Ukraine conflict will not happen and it will be more cyber warfare?

Asaf - I think there are more challenges, but I think cybersecurity issues are gaining more value because more assets are converting to be online and again, our daily things are online, and sometimes even from the army's perspective, its easier to do something behind the keyboard and not blast anyone and risk your people going to war, I think it's a future threat. While saying it, I think it's also there is a balance thats kind of the nuclear balance that both of the sides have nuclear power, so no one uses it. I think it might go to this one. Maybe the country could devastate the other country in the cyber war, but I think they understand that there might be attacks on the same power as well. It might be a threat that is above our head, but not really will be done with 100% power. I do think and we already saw that in a low power cyber attacks already happening right? Also, even in Ukraine and Russia conflict, Russia on the starting day of the conflict, wiped out hundreds of machines and denial of services attacks on websites in Ukraine and so on.

Siddharth - Asaf now lately, we have seen Predator, we have seen ERMAC, Follina, and a lot of other malware or ransomware coming out. Why is this happening? We have the internet the knowledge is there, and the news spreads. Yet all these things are happening so much more today than say 5 to 10 years ago.

Asaf - Yes, with the popularity of the internet and technologies and phone devices and everything is computerized, and I think that awareness and even the availability of knowledge is very easy to gain for everyone.

As an example, the lapsus$ or phishing attack, will still probably work. It is really a problem, but again we should sleep well, there are cybersecurity vendors out there doing our best. For example, at CyberArk, we try to help organizations across the world and so it will be harder for attackers to achieve their goal also if they attack a company, the damage will be reduced a lot. They will not be at a total loss, and we also see this from the other side of your question, we see more attacking groups yes, and ransomware campaigns because they have money and there are more options for people and also to build an organization and a business. As an example, there is Conti, an attacking group that does ransomware mainly. It's built like a regular company, there is human resources, HR, there is R&D, and there is a kind of marketing to make the tool available to a paying audience. Yeah, this is kind of the new world.

Siddharth - You mentioned the supply chain attacks. Now if the supply chain is crippled for a big company like say Samsung or Apple. It is going to cause a lot of damage and damage reduction will be the biggest thought once the attack has happened. What are the things they should keep in mind before an attack happens and after an attack happens to minimize the damage to them and their consumers?

Asaf - Before the attack happens, we should make sure that our network is there in the most secure place and in the most secure state. There are many protocols and steps and standards. One of the main things is to secure privileged access security and secure identity security. Nowadays, it's not only devices and laptops and phones, but also more of the identity that uses this computer in this form because it could be many identities on the same device, and multiple identities or specific identities could be accessing across multiple devices. We need to secure the focus on identity. How it has been authenticated, what it does and there are again many solutions that can help with this. I would focus on securing the identities and of course making sure to check all the standards.

If we do the preparation right in stage 1, the damage will be limited because one identity will be compromised and one network will be compromised, but the sensitive database is on a different network and there is a segmentation in the network, and so on. If we did the preparation right then the damage should be limited, but still, we should also prepare for this compromise because it might happen at any time and we should also practice it. I think most organisations will suffer from this kind or another compromise, but good preparation will limit the damage when it occurs.

Siddharth - What would your forecast be in terms of trends of security that we will be seeing in the future, like supply chain is one, next what could be it?

Asaf - Interesting question. I think cloud will be major as nowadays cloud is a popular for the technology benefits and so on and I think now that cloud services are being used much more, the attacks on this kind of scenario will be more popular. Some unique specific services like database on cloud and SQL on cloud and virtual machine on cloud and things like this.

Another thing I might say is the attacks in the future will be about machine learning and automation around attacking and automation around the discovery of vulnerabilities, open source is also a popular vector because nowadays because technology is so complex, we have several components on every product. Open source is also another vector.

Siddharth - During WWDC, Apple announced something about a passwordless feature. Do you think this is an interesting concept that will increase security?

Asaf - Yes. There are several disadvantages of having a password. Of course, its hard to remember, people tend to use the same password for 2 different services and so on. The trend of a passwordless future, I think it's good. Mainly it involves some other device or multifactorial with your phone. The passwordless thing is a good solution, From our vulnerability research, we saw after the authentication has been successfully done, it's still a token or digital token or certificate that is being stored in the computer and a device in the cloud. After the authentication phase, the token is not really authenticated more. Nowadays there is a new trend of continuous authentication. You want to continuously authenticate the identity and what it does.

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The most important cryptocurrency event in years is about to begin and the biggest windfall goes to the planet – The Conversation

Amid the continuous noise about cryptocurrencies, its often hard to pick out what really matters. However this month, if all goes to plan, the energy-hungry digital sector will undergo its biggest shake-up in years.

Ethereum, the worlds second largest cryptocurrency, is on Tuesday expected to start a technology changeover which, once complete, should cause its carbon emissions to plummet by 99%.

The rapid growth in cryptocurrencies in recent years has been staggering. Unfortunately, so too has been their contribution to climate change, due to the enormous amount of electricity used by computers that manage the buying and selling of crypto coins.

Take, for example, the worlds biggest cryptocurrency, Bitcoin. At a time when the world is desperately trying to reduce energy consumption, Bitcoin uses more energy each year than medium-sized nations such as Argentina. If the Ethereum switch succeeds, Bitcoin and other cryptocurrencies will be under immense pressure to deal with this problem.

Cryptocurrencies are digital currency systems in which people make direct online payments to each other.

Unlike traditional currencies, cryptocurrencies are not managed from a single location such as a central bank. Instead, theyre managed by a blockchain: a decentralised global network of high-powered computers. These computers are known as miners.

The Reserve Bank of Australia provides this simple explanation of how it all works (edited for brevity):

Suppose Alice wants to transfer one unit of cryptocurrency to Bob. Alice starts the transaction by sending an electronic message with her instructions to the network, where all users can see the message.

The transaction sits with a group of other recent transactions waiting to be compiled into a block (or group) of the most recent transactions. The information from the block is turned into a cryptographic code and miners compete to solve the code to add the new block of transactions to the blockchain.

Once a miner successfully solves the code, other users of the network check the solution and reach an agreement that its valid. The new block of transactions is added to the end of the blockchain, and Alices transaction is confirmed.

This process, used by most cryptocurrencies, is termed proof-of-work mining. The central design feature is the use of calculations which require a lot of computer time and huge amounts of electricity to perform.

Bitcoin alone consumes around 150 terawatt-hours of electricity each year. Producing that energy emits some 65 million tonnes of carbon dioxide into the atmosphere annually about the same emissions as Greece.

Research suggests Bitcoin last year produced emissions responsible for around 19,000 future deaths.

The proof-of-work approach intentionally wastes energy. The data in a blockchain has no inherent meaning. Its sole purpose is to record difficult, but pointless, calculations which provide a basis for allocating new crypto coins.

Cryptocurrency advocates have given a variety of excuses for the monstrous energy consumption, but none stand up to scrutiny.

Some, for example, seek to justify cryptocurrencys carbon footprint by saying some miners use renewable energy. That may be true, but in doing so they can displace other potential energy users some of whom will have to use coal- or gas-fired power.

But now, the most successful of Bitcoins rivals, Ethereum, is changing tack. This month it promises to switch its computing technology to something far less polluting.

Read more: Ethereum: the transformation that could see it overtake bitcoin

Ethereums project involves ditching the proof of work model for a new one called proof of stake.

Under this model, crypto transactions are validated by users, who stake substantial quantities of blockchain tokens (in this case, Ethereum coins) as collateral. If the users act dishonestly, they lose their stake.

Importantly, it will mean the vast network of supercomputers currently used to check transactions will no longer be required, because users themselves are doing the checking a relatively easy task. Doing away with the computer miners will lead to an estimated 99% drop in Ethereums electricity use.

Some smaller cryptocurrencies such as the Ada coin traded on the Cardano platform use proof of stake but its been confined to the margins to date.

For the past year, Ethereum has been running the new model on experimental blockchains. But this month, the model will be merged into the main platform.

So what does all this mean? The Ethereum experiment could fail if, say, some stakeholders find ways to manipulate the system. But if the switch does succeed, Bitcoin and other cryptocurrencies will be under pressure to abandon the proof-of-work model, or else shut down.

This pressure has already begun. Tesla founder Elon Musks last year announced his company would no longer accept Bitcoin payment for its electric cars, due to the currencys carbon footprint.

The New York state legislature in June passed a bill to ban some Bitcoin operations that use carbon-based power. (However, the decision requires sign off from New Yorks governor and may be vetoed).

And in March this year, the European parliament voted on a proposal to ban the proof-of-work model. The proposal was defeated. But as Europe heads into the cooler months, and grapples with an energy crisis triggered by sanctions on Russian gas supplies, energy-guzzling cryptocurrencies will remain in the firing line.

One thing is clear: as the need to slash global emissions becomes ever more pressing, cryptocurrencies will run out of excuses for their egregious energy use.

Read more: Tesla's Bitcoin about-face is a warning for cryptocurrencies that ignore climate change

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The most important cryptocurrency event in years is about to begin and the biggest windfall goes to the planet - The Conversation

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Russia wants to use cryptocurrency to skirt sanctions, but is it practical? – Ledger Insights

Yesterday Russias Deputy Finance Minister Alexei Moiseevsaidthat the Central Bank of Russia, which has resisted moves to legalize cryptocurrency, was willing to contemplate using cryptocurrency for cross border payments.

To date, the Ministry of Finance and the central bank have taken opposing positions regarding cryptocurrency, with the central bank objecting to proposed Ministry of Finance legislation to legalize the sector.

Russian news outletRIA Novosticontacted the central bank, which confirmed that it was only discussing cryptocurrency to settle cross border transactions. It emphasized the discussions are not about legalizing cryptocurrency within the country for payment nor for cryptocurrency exchanges.

Another newspaper,Kommersant, explored the practicality of using cryptocurrency to settle corporate payments. One businessman runs a fashion house and mainly deals with French and Italian companies. He was unconvinced his business partners would be technically comfortable dealing with cryptocurrencies as many are family companies.

Another manages a Moscow furniture store and was personally concerned about cryptocurrency volatility and viewed them as a toy for speculative investors. However, he mentioned the possibility of stablecoins but was aware of the collapse of Terra.

The real question is whether or not the effort is worthwhile because foreign regulators are likely to respond. Cryptocurrency exchanges might be loathe to breach sanctions for fear of being denied licenses elsewhere. And self-hosted wallets might prove a step too far for corporates unfamiliar with the technology.

The transparency of cryptocurrencies and blockchain means the Russian destination for payments is likely to become obvious unless they are routed elsewhere, such as via Hong Kong.

Given discussions between the Ministry of Finance and the central bank are still ongoing, a range of possibilities could be pursued. These include a bespoke digital asset, even a digital ruble.

The Central Bank of Russia has acknowledged that it has accelerated work on a central bank digital currency and sees it as away to circumvent SWIFT. Earlier this month, it said pilots would start in 2023. Additionally, in June, state-owned defense firmRostecannounced it had developed an alternative to SWIFT in international settlements.

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Whats the Cryptocurrency Market Like in Cuba? – Havana Times

By Alberto C. Toppin (El Toque)

HAVANA TIMES Many Cubans hadnt heard of cryptocurrency, four years ago. They were just about beginning to have Internet access and they didnt know words like bitcoin and blockchain.

However, people are talking about a cryptocurrency market today, with users from every province. Twitter account Space spoke about the development, prospects and challenges for this market on How is the cryptocurrency market in Cuba?, with input from business founders linked to the crypto world.

Interviewees pointed out many reasons for why cryptocurrency was adopted in the beginning as a form of payment. The current cryptocurrency situation is linked to Cubas informal market of the dollar and euro, and is a consequence of measures implemented in 2019 with the opening of stores that only accept MLC (a magnetic currency used with prices in USD), Adrian C. Leon weighed in, the founder of Fusyona, one of the first exchanges that offered cryptocurrency to Cuban customers this year.

The decision to introduce MLC left informal buyers & sellers without access to the currency they needed to travel and affected their businesses to a great extent, Leon continues, talking about the people who would leave Cuba to buy products they then sold on the island. With their businesses hit hard, both sellers and other Cubans chose to emigrate, and to do that they needed to withdraw over the 5,000 USD which Customs allows you to take in cash. Peoples best bet was cryptocurrency, he pointed out.

According to Adrian C. Leon, the migration crisis that has persisted for years has meant that more Cubans are outside of the country and need to send remittances back to their families on the island. People who knew about cryptocurrency are still using it, he says, referring to crypto as a means to send money to Cuba without going through Government hands.

Transfer of value is the main focus given to cryptocurrency in Cuba today, which is its inherent nature, explained Erich Garcia Cruz, founder of BitRemesas and QvaPAy; two well-known Cuban businesses that work with cryptoactives. This transfer of value translates into remittances, in reserve. There are many people who prefer to save their incomes or buy cryptocurrency to hustle, he added.

Garcia Cruz also revealed other benefits of using cryptocurrency in the Cuban context, especially in the business world. I like people to also discover all of the potential crypto has to set up virtual stores, to buy or sell services online or products in and outside of Cuba for Cubans, he said.

However, he added that these benefits arent being capitalized on yet among the business community. Right now, the focus is on sending remittances or financial speculation between themselves, in regard to MLC, he pointed out.

The influencer also advocated for the use of crypto as a channel to for businesses to grow, not as a means to an end. Cubans with CUP (regular pesos) couldnt buy anything on the Internet before; now, a Cuba with CUP can, via a service.

According to Mario Mazzola, founder of the Qbita exchange, the existence of unclear investment schemes also led to many Cuban fans of cryptocurrency. The reality is lots of people were scammed, but it played a very important role in teaching people about the subject. Maybe people dipped their toes in this world for the wrong reason, with the idea of magically making millions, but they learned a lot of useful things about technology, about how it works and how to manage a digital wallet.

Mazzola also pointed out that the idea of looking for millions with cryptocurrency led some Cubans to invest their money in cryptoactives and wait for the value to go up, an investment strategy called holding. It worked for some people; others didnt understand what the real value of this technology was in the beginning, they let themselves follow promises of magic profits, and we know how that story ends.

During Space, Abraham Calas, elTOQUEs development and innovation director, presented the updated representative rate for cryptocurrency on the informal market, as well as a calculator to understand the equivalent in cryptocurrency of a certain sum in Cuban money (CUP or MLC). The calculator uses the values of the rate as a reference.

In regard to tolls and challenges for adopting cryptocurrency, Cuban businessman Luilver Garces pointed out that you should always seek out final products that are digestible. If you are able to get a child to understand what cryptocurrency is, youve won; if you are able to get your grandmother to understand what cryptocurrency is, youve won but you wont until this happen, theres still a lot of work to be done, he said.

One of the interesting things that can be seen the most in the Cuban crypto market is the dollar being taken as a fraction of cryptocurrencies. On this subject, Erich Garcia Cruz said that this phenomenon happens because of peoples fear of the volatility of prices. People always structure buying or selling prices against the dollar.

This experience has taught me that many people have come from using other currencies, not necessarily crypto, Liuver Garces explains, referring to the job of platforms such as PayPal. The phenomenon of putting cryptocurrency against the dollar as a reference during operations maybe comes from the fact people started off talking in dollars, he says.

About differences between prices on the Cuban and international markets, Adrian C. Leon explained that its a consequence of the decentralization of cryptocurrency. The price of bitcoin in Cuba has nothing to do with the price in the US, with the price in Brazil, and this is going to happen with other countries.

Cryptocurrency hasnt been adopted on a mass scale in Cuba yet and Mario Mazzola has pointed out that this may be because of the way it works, as it focuses on peer to peer operations and you need a digital wallet. It isnt so easy for somebody who isnt highly motivated to embark on this learning journey.

According to Mazzola, the lack of trust that unclear investment schemes created also had an impact and meant that people decided not to use cryptoactives, in addition to price volatility.

Liulver Garcia belives that peoples late access to the Internet on a mass scale in Cuba, in 2018, has meant that there isnt a lot of know-how on subjects linked to blockchain and cryptocurrency such as bitcoin. First of all, we need to computerize the country, learn a whole load of concepts that werent born yesterday with the Internet from four years ago up until now, but that exist and existed even before the Internet, such as privacy and data protection, he explained.

The challenge is more educational rather than technological, Erich Garcia Cruz pointed out. There are processes with cryptocurrency that dont exactly require actors to know what a private key is or how to encode a transaction on the blockchain, he said. Plus, he cited the Moon Wallet a digital wallet that allows instant bitcoin transactions as an example, and the lack of knowledge about how it works exactly.

In the last section of Space, some interviewees offered their point of view on what would be the best step to take in a context where bitcoin has very low prices. If the bitcoin is below 20,000 right now which isnt the case, its higher -, Id wait for it to go down to 15,000, for example, to go out and buy; and if I have bitcoin I bought cheap, Id wait for it to go to 30,000, Luilver Garces said.

If you believe in bitcoin, its better to buy it when its worth 20,000 and not when its worth 68,000, Mario Mazzola recommended, explaining that it isnt a good idea to sell personal assets to do this.

My greatest recommendation is always to follow the basic rule of trading (buying/selling to make a profit), which is never to invest any more than youre willing to lose, Adrian C. Leon, founder of Fusyona, said.

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Cryptocurrency price today: Bitcoin slips 6%, Polygon MATIC, Avalanche down over 10% as crypto markets bleed red – Business Today

Cryptocurrency prices are in the deep red this Wednesday morning. The global cryptocurrency market cap is down by 6.14 per cent from Tuesdays levels. The cryptocurrency data aggregator CoinMarketCap showed that the global crypto market cap is at $942.50 billion. The total cryptocurrency market volume over the last 24 hours stands at $88.80 billion, 44.67 per cent higher than Tuesday. Bitcoins dominance is at 38.23 per cent, a decrease of 0.08 per cent over the day.

Bitcoin, Ethereum, and BNB

Mainstream cryptocurrencies like Bitcoin, Ethereum, and BNB have taken a steep dip over the last 24 hours.

Bitcoin, the largest cryptocurrency by market cap, is trading at $18,835 and is down by 6.11 per cent. Ethereum blockchain networks Ether showed a downtrend of 7.72 per cent. The cryptocurrency token is trading at $1,532. BNB crypto, native to Binance Smart Chain, witnessed a downtrend of 7.56 per cent.

USDT, USDC, BUSD, and DAI

Stablecoins USDT and USDC have gained over the last 24 hours while BUSD and DAI have slipped down.

USDT Tether stablecoin gained 0.01 per cent in its value over the last 24 hours and is trading at $1. USDC witnessed a 0.01 per cent positive change in its value over the last 24 hours. The stablecoin is trading at $0.9999.

BinanceUSD or BUSD slipped 0.02 per cent and is trading at $1. Stablecoin DAI fell 0.05 per cent and is trading at $0.9994.

Layer 1 blockchain tokens

Cryptocurrency tokens native to Layer 1 blockchain networks like Solana, Ripple, and Avalanche also witnessed a downtrend over the last 24 hours.

Cardano's ADA token is down 8.94 per cent. Solana blockchain networks SOL fell by 6.87 per cent. Avalanche's AVAX slipped by 10.38 per cent. Ripple blockchains XRP has fallen 6.23 per cent.

Polkadot and Polygon

Polkadot blockchain networks DOT token is in the red. The cryptocurrency token is down by 9.78 per cent. Polygons MATIC crypto token has slipped by 10.36 per cent over the last 24 hours.

Memecoins

Both mainstream meme crypto coins, Dogecoin and Shiba Inu witnessed a huge downtrend. Dogecoin is down a whopping 8.66 per cent, whereas meme crypto Shiba Inu is down a significant 6.53 per cent over the last 24 hours.

Overall, the majority of top cryptocurrency tokens have slipped down from their previous positions over the last 24 hours.

Also Read:Cryptocurrency price today: Ethereum, Polygon, Cardano in green; Bitcoin, memecoins Doge and Shiba Inu down - BusinessToday

Also Read:Cryptocurrency price today: Bitcoin, Ethereum, Shiba Inu in green, Dogecoin down; majority tokens gain - BusinessToday

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Cryptocurrency price today: Bitcoin slips 6%, Polygon MATIC, Avalanche down over 10% as crypto markets bleed red - Business Today

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