If there is one thing all regional technologists agree on, it is the presence of a shortfall in high–end talent. Digital skills in general are in short supply, but the ratio of available roles to qualified candidates for expert positions is particularly high. Across the region, governments, businesses, and non–profits are crying out for artificial intelligence (AI) specialists and data scientists. The future depends upon leveraging AI to solve problems that humans, on their own, cannot.
Objective 5 of the UAE’s National Strategy for Artificial Intelligence 2031 focuses on the attraction and training of AI talent. The country’s AI ministry is intent on delivering AI training courses to government employees and the working public, as well as upskilling students by selecting one third of the nation’s STEM graduates each year – which is around 2,000 people – for special training.
The UAE has long been a proponent of leveraging AI to do just about anything that will upgrade society and governance. Its AI ministry was the world’s first, and the government’s laser focus on using smart technologies for everything from smart grids to public health will require a bedrock of talent in the years to come.
A path forward Acquiring the skills to implement all the AI projects in the pipeline currently means engaging the services of either a third–party software company or a consulting firm, but rarely do either of these improve the talent pool of a company in the long term. Once these options are discarded, recruiting companies are left with hiring the best data scientists available, but the talent acquired may not be of the desired quality.
Continue reading: https://gulfbusiness.com/the-ai-talent-factory-what-it-is-and-how-to-build-it/

It’s predicted that 97 million jobs involving artificial intelligence (AI) will be created between 2022 and 2025. AI has the potential to transform every industry; however, businesses are still struggling to find employees with the skills necessary to create, train and work alongside intelligent machines.
As companies have become aware of the efficiency gains that can be achieved through leveraging the power of machine learning, computer vision, and similar technologies, demand for skilled workers in the field is quickly outstripping supply.
Colleges and universities have responded to this by creating new courses and educational programs focusing on these skills. But anyone wanting to break into the industry may still be confused at the options available to them. So here’s a run-down of some of the most valuable skills you can learn today if you want to be prepared to work with the automated, intelligent machines of the future!
Programming
Although no-code and low-code AI solutions are appearing that let us leverage AI solutions without getting our hands dirty, it’s likely that businesses that want to deploy their own bespoke AI solutions will require skilled coders for a long time yet. A basic understanding of at least one of the most popular programming languages for AI - Python, R, C++, and Java – is very useful for anyone working with machine learning algorithms. This may seem a little counter-intuitive – as the purpose of AI is to enable computers to “learn” without having to be specifically coded to carry out a job. Nevertheless, most people working in roles that involve AI today will recommend that some level of experience in coding is highly valuable for anyone wanting to prepare themselves for using AI.
Data Science
Data is absolutely fundamental to the ability of machines to think and learn. Data is the input used to train AIs to make decisions and carry out tasks. Data scientists understand how to capture, manipulate and work with data in order to extract insights from it. These skills are essential to the field of AI because they encompass the advanced analytics that are necessary in machine learning algorithms. Data science has been a part of computer science educational curriculums for a long time, and today they are usually heavily focused on applying AI to solving business problems using available information.
Continue reading: https://www.forbes.com/sites/bernardmarr/2022/06/13/what-are-the-most-in-demand-ai-skills/?sh=73f65a0b249c

Artificial intelligence (AI) has been a mainstay of science fiction for many years. As long ago as 1872, science fiction writers depicted computers that controlled the earth — robots that challenged or aided humanity or human battles against AI machines. Think “2001: A Space Odyssey,” “Blade Runner” or “The Matrix.”
However, while science fiction writers played with artificial intelligence, AI marketing has become an increasingly important tool for companies in the real world.
In 2021, the global AI market was valued at $93.53 billion — a number expected to reach $997.77 billion by 2028, according to Grand View Research.
In 2022, Statista published a report that said, "AI is expected to have wide adoption in and implications for every industry vertical and is likely to be one of the next great technological shifts, like the advent of the computer age or the smartphone revolution."
How Does Artificial Intelligence Work in Marketing?
AI marketing typically fits into two categories: task automation and intelligent/machine learning. And these two use categories can operate either as standalone or integrated programs.
Task Automation vs. Intelligent AI
Task automation is pretty straightforward — AI programs carry out structured, repetitive tasks. They operate according to a pre-determined set of rules or programmed sequence of events. This use of AI is not, and need not be, intelligent.
Intelligent AI marketing, however, takes advantage of machine learning — a type of AI that can become more accurate over time, learning as it goes, essentially. Intelligent AI runs large quantities of data through its pre-programmed algorithms to make complex predictions and decisions.
Intelligent AI places customers in verticals that best reflect their interests and anticipates how they will respond to promotions, discounts or seeing a product or service that corresponds to their customer preference profile.
However, these programs aren't perfect. Most applications can only perform in narrow areas, and each use case needs direction on how to compute large amounts of data.
Continue reading: https://www.cmswire.com/digital-marketing/can-ai-marketing-transform-your-business/

Recent advances in artificial intelligence (AI) have catalyzed a sea change in the ability of computers to engage in live, human-like conversation with people. Today we are seeing conversational AI systems with unprecedented sophistication in their ability to converse in a manner that is practically indistinguishable from speaking with a human. Going far beyond Alexa or Siri responding to a simple query, today’s state-of-the-art conversational AI (CAI) systems navigate complex dialogue, understand nuanced intent, navigate multi-topic, multi-turn conversations and articulate cogent, on-point responses. The rise of CAI is creating a new paradigm for customer experience, personalization and productivity in the modern workplace. As a venture capital investor who focuses on AI and business software, I have been privy to the breakthroughs in CAI and invested in industry-leading companies.
Back in the 1960s, researchers at MIT pioneered ELIZA, an early natural language software, that attempted to simulate human dialogue. However, ELIZA and programs like it were severely limited in their real-world applicability. These systems were programmed with brittle rules and canned responses and could only handle a very narrow set of use cases without breaking down.
By contrast, today’s CAI systems are powered by machine learning, giving them far greater dexterity as well as the ability to self-improve over time. The revolution in CAI has benefitted from an abundance of data as well as the continuously improving price-performance of compute—both necessary ingredients to train good AI systems. And, crucially, recent years have seen extraordinary funding and talent funneled toward developing groundbreaking large language models, which provide a core foundation for the development of conversational AI applications.
Continue reading: https://www.forbes.com/sites/forbesbusinesscouncil/2022/06/10/how-conversational-ai-is-supercharging-business-productivity-and-how-to-get-started/?sh=1e7b4c332645

Organizations recognize that they need a systematic approach to “operationalizing” AI in order to drive AI success.
The pace of digital transformation has massively accelerated with artificial intelligence (AI) technologies and is already transforming every aspect of business. With massive improvements in storage systems, processing speeds, and analytic techniques, we’ve reached an inflection point where AI and cloud technologies are enabling tremendous sophistication in analysis and decision making.
But the reality is, despite the hype around AI, the majority of companies are still either kicking their heels or simply failing to get AI strategies off the ground.
But why do so many AI projects fail?
The road from data to successful data-driven, AI- and ML-powered projects is no straight line. There are many variables that go into building effective artificial intelligence, which makes it difficult to prescribe set steps that will work well every time for every company. It is known that launching pilots is deceptively easy but deploying them into production is notoriously challenging. This is the reason why despite the early adoption of AI by many organizations, few have managed to reap consistent business value on their AI investments.
Reasons for AI failure – The Known and Unknowns
AI is still seen as risky business — an expensive tool that’s difficult to measure and hard to maintain. Organizations often start AI projects based on competitive pressures, fearing they will be left behind if they are not investing in AI. Not understanding what AI can be best used for is a top reason for failure. Other challenges can be attributed to reasons such as lack of a cohesive artificial intelligence strategy, no collaboration between business and IT stakeholders, poor organizational alignments, slow and complex implementations, and lack of continued C-suite commitments.
Continue reading: https://www.rtinsights.com/addressing-artificial-intelligence-unknowns-to-achieve-success/

In March 2019, Norsk Hydro, a Norwegian renewable energy and aluminum manufacturing company, faced a ransomware attack. Rather than paying the ransom, a cybersecurity team used artificial intelligence to identify the corruption in the computer system and rebuild the operations in an uncorrupted parallel system. LockerGoga ransomware was eventually identified as the culprit, which spread via Windows-based systems. While Norsk avoided paying the ransom, the attack still forced it to operate without computer systems for an extended period of time (weeks to months), while the security team isolated and scanned thousands of employee accounts for malicious activity.
Signature-based detection is an approach in which a unique identifier is established about a known threat so that it can be identified in the future. However, signature-based approaches require continuous updates that take time and effort to maintain. Next-generation artificial intelligence (AI) products learn proactively and identify changes in the networks, users, and databases through what is called data drift to adapt to specific threats as they evolve.
AI products are the linchpin of a multifaceted defense system that can be utilized in the background prophylactically, especially against unknown threats. Cyberattacks that make the evening news are usually the ones that end in disaster; it is hardly ever reported how AI could have prevented those attacks in the first place. In addition, cyberattacks that are contained or thwarted on a daily basis, while AI is ubiquitously at work, are almost never reported in the news because they happen so frequently.
Unfortunately, as a result of the lack of coverage on these "non-events" in public forums, most people don't understand how AI makes an effective cyber defense achievable and not just theoretical. Here is what you should know.
Continue reading: https://www.darkreading.com/attacks-breaches/artificial-intelligence-and-security-what-you-should-know

Artificial intelligence is completely overhauling the way the world functions. Whether in education, healthcare, or workplaces, AI is driving significant changes to minimize human effort. Talking about the corporate world, in particular, technology innovation provides excellent scope for active employee engagement. Cooperation, team-building, and communication are key features of a high productivity culture. But it is often seen that many organizations fail to promote team-building for various reasons. Even workers are not able to develop mutual trust in each other. This is where artificial intelligence proves to be a useful, easy, and enjoyable tool for building cordial workplace relationships. According to Narrative Science, more than 60 percent of businesses had already implemented AI by 2017. In 2020, this number would have hiked even further. Companies are happy to incorporate this new team member!
This blog illustrates the various ways in which artificial can help in team-building. Before we move to that, let us take a quick look at what hinders collaborative efforts among employees.
Common hurdles in the way of effective team building

1. Lack of trust: For any relationship, be it personal or professional, faith is the only pivot. Without trust, people exhibit a lack of confidence in each other while working on common goals. But that is what teams are supposed to do! Teammates have to trust each other when they work in collaboration for the organization’s success. In the absence of mutual trust, the process of team building remains incomplete. Besides, it is common to see that team members do not show faith in a recruit to handle challenging scenarios.
2. Communication Gaps: Another reason why teams fail is the absence of clear communication between team members. If teammates will not convey their ideas to each other, there is a sense of directionless functioning. Subsequently, the work efficiency of the team and the organization takes a blow.
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   Continue reading: https://original.newsbreak.com/@jessica-robinson-1589778/2633518529521-5-ways-in-which-artificial-intelligence-facilitates-team-building

A career in recruitment is the essence of finding a needle in a haystack. While there are plenty of reasons why this is true, a few include increased employee demand in particular segments, a company’s economic stats going haywire and a surplus of candidates hunting for the same job.
Lately, numerous talent acquisition professionals are reaping the benefits of AI-driven technologies. In fact, automated resume screening software has become an integral part of many recruiting systems globally.
According to the data from Predictive Hire, nearly 55% of companies are investing in recruitment automation and believe that it’ll enhance efficiency and enable data-driven judgments. For instance, a resume parser, a technology I work with extensively, helps screen resumes and extract candidate data. For the recruiters who are still in limbo about whether or not to go for augmented AI, I’ve lined up a few benefits that can be helpful as well as some best practices.
Carrying a passion for bringing innovative solutions to the HR Tech industry, I spend most of my time with enterprises, mentors and thought leaders to understand bottlenecks in the recruitment process. That’s why I understand how error-prone recruitment can be.
Understanding the vital role automation plays in the HR industry and how well it caters to the challenges, there are certain solutions that can make these automated tools more affordable and accurate.
Continue reading: 
https://www.forbes.com/sites/forbesbusinesscouncil/2022/06/10/artificial-intelligence-in-hiring-a-tool-for-recruiters/?sh=238508ec3200

AI violates privacy. AI-generated output can’t be explained. AI is biased.
This is all true, and is happening today, and there’s a risk of these issues accelerating as AI adoption grows. Before the lawsuits start flowing and government regulators start cracking down, organizations using AI need to become more proactive and formulate actionable AI ethics policies.
But an effective AI ethics policy requires more than some feel-good statements. It requires actions, built into an AI ethics-aware culture. “An AI ethics statement is a nice start. It’s also the tip of the iceberg,” relates Reid Blackman, who explores AI ethics in his upcoming book, Ethical Machines: Your Concise Guide to Totally Unbiased, Transparent and Respectful AI (Harvard Business Review Press). “Even those who spend a lot of time talking about and even working in the field of AI ethics have a lot of difficulty understanding what’s to be done. That’s because they’re trying to build structure around something they still find squishy, fuzzy, and subjective.”
Blackman, CEO of Virtue, seeks to turn something as “squishy” as AI ethics into something concrete. He provides guidelines to instilling actionable ethics into AI systems and processes.
Continue reading: https://www.forbes.com/sites/joemckendrick/2022/06/10/7-steps-to-more-ethical-artificial-intelligence/?sh=5534d8254c45

Cybercrime is a serious threat to any organization, with data breach costs reaching over USD 4 million on average (Mack, 2021). Companies today face many potential cyber risks each year, and the results can be catastrophic.
Since Internet of Things (IoT) devices often face attacks as soon as 5 minutes after connecting, they can present a severe vulnerability (Jovanović, 2022). The number of devices connected to the internet has skyrocketed and now includes many medical devices, smart home components, and even e-cigarettes.
With the increase in IoT devices, IoT-related cyberattacks have also grown, giving rise to the field of IoT forensics. What does digital forensics mean in the IoT context, and what is its purpose?


What Is IoT Forensics?
IoT forensics is the practice of analyzing IoT devices to investigate crimes. Organizations or law enforcement may hire experts to gather and preserve data when investigating whether hackers used internet-connected devices to commit cybercrimes or examining the source of a security breach.
In some instances, breaches occur due to malicious intent. In other cases, they may result from human error—for example, if an employee shares sensitive information due to a phishing attack. The employee may have had no intention to steal data or harm the company, yet the results of sharing that data accidentally can be just as catastrophic. These phishing attacks cause nearly nine out of 10 data breaches (Cisco Umbrella, 2021).
Cyber forensics can help determine the exact intent and extent of a breach and much more. Typical IoT-related cyberthreats may include:
Continue reading: https://www.eccouncil.org/cybersecurity-exchange/computer-forensics/understanding-meaning-purpose-iot-forensics/

Data centers are continually driving to become more efficient, and to get more useful work out of staff. In the case of giant hyperscale data centers, increasing automation may be calling time on the practice of people patrolling thousands of yards of data center perimeters.
Novva data centers, led by former C7 CEO Wes Swenson and backed by CIM Group, has a 100-acre flagship in West Jordan, Utah - a campus that could reach more than 1.5 million sq ft (139,350 sq m) of data center space. The first phase, involving a 300,000 sq ft (28,000 sqm) data center was completed in late 2021 and includes a 120MW substation as well as an 80,000 sq ft (7,500 sq m) office building for Novva’s headquarters.
To cover that much space, Novva is turning to autonomous drones and robots. The company is deploying Boston Dynamics’ Spot robot to patrol data halls, as well as semi-automated security drones (also known as Unmanned Ariel Vehicles, or UAVs).
“When you run a 100-acre campus, you really should have an aerial view of the operation,” says Swenson.
Drones take flight at the data center
In 2021, Novva deployed two Blackbird drones from Nightingale Security – and plans to have four on the Utah campus.
“For the most part, it just does its own thing and then just autonomously goes back to its landing site,” says Swenson.
Continue reading: https://www.datacenterdynamics.com/en/analysis/drones-at-and-even-in-a-data-center/

According to Crypto.com, global cryptocurrency usage numbers doubled within four months in the first half of 2021. As more consumers begin adopting crypto, small businesses will have no option but to rise and meet the occasion.
Today, several businesses have already done so, making them well-positioned to meet the ever-increasing needs of the crypto-verse. In this article, you’ll learn how to begin accepting crypto payments in your small business. Read on for more!
Accepting Crypto Payments as a Small Business
A nationwide survey conducted by HSB in 2020 showed that close to 36% of all registered small businesses had begun accepting crypto payments. If your business isn’t in this bracket, it’s high time you did so to help you catch up with your competition.
Of the business owners who took part in this survey, 50% said they got motivated to adopt crypto after seeing its adoption by large innovative companies and major payment processors. 35% said that they adopted crypto payments to meet customer demands.
While some small businesses have chosen to adopt crypto payments to catch up with their competition, others have done so to become more efficient. The reality is that there are many benefits to using cryptos, e.g., bitcoins in small businesses. Some benefits include:
Continue reading: 
https://www.benzinga.com/markets/cryptocurrency/22/05/27284860/how-you-can-accept-crypto-payments-as-a-small-business

Blockchain technology is a shared database that records transactions in a network of blocks that are linked by cryptography. Every transaction in this ledger has the owner’s digital signature, which verifies the transaction and protects it from tampering. Once a new transaction is made, the data is attached to the previous block, forming a chronological and immutable chain of records. This technology enables for the recording and distribution of digital information, but not for editing.
The most common application of blockchain has been for financial transactions. However, gradually the technology has expanded to other fields, including government activity. Due to its properties, it assures that any copy of the data is always available, verifiable, and trustworthy.
The European Blockchain Partnership (EBP) launched in 2021 has one initiative that is worth mentioning – the European Blockchain Services Infrastructure (EBSI). According to their strategy document, EBSI is a peer-to-peer network of interconnected nodes that runs a blockchain-based services infrastructure. At least one node will be run by each member of the EBP – the 27 EU nations, Norway, Liechtenstein, and the European Commission.
The infrastructure in question is made up of different layers including:
Continue reading: https://www.mercurynews.com/2022/06/10/blockchain-based-digital-government/

Blockchains are touted as next generation databases that promise to facilitate secure and efficient transactions between unknown parties. However, one of the primary pillars of a blockchain’s security is the fact that people with access to the blockchain can see the entire history of transactions executed on the blockchain – the result being that each party has an equal opportunity to verify the accuracy of information stored. But if all the information stored on the blockchain can be viewed by anyone with access to the blockchain, what happens when that information qualifies as “personal information” under Canadian privacy laws? Organizations that collect use or disclose “personal information” are subject to a variety of compliance obligations, which as we set out below, can be difficult to reconcile with certain blockchain fundamentals.
What is personal information?
In Gordon v Canada, the Federal Court explained that personal information is information that can be used to identify an individual if the information “permits” or “leads” to the possible identification of the individual, whether on the basis of that information alone, or when the information is combined with other information from other available sources.1 Accordingly, a company that merely “de-identifies” or “pseudonymizes” data may still be subject to Canadian privacy law requirements because there is a possibility that such data can be “re-identified”. This poses a unique challenge to the developers of blockchain infrastructure, and the businesses that operate atop blockchain infrastructure, when the metadata that is necessarily ingrained in blockchain transactions may be re-identifiable. Such metadata may constitute personal information when it reveals where transactions are sent from, who they are sent to (not necessarily the name of the recipient, but the address of the recipient), how much money was sent, and at what time.
Take decentralized applications (DApps) for example, which are built from software deployed on the blockchain (e.g., smart contracts) that are typically designed to execute business operations for companies.2 The operations of the smart contracts that effectively facilitate the functionality of the DApps are often made publicly available to every node in the blockchain network as “bytecode”, which can be reverse engineered to reveal the same transactional information as metadata in peer-to-peer transactions.
So, what does it mean if such data, stored and processed on public blockchain networks, qualifies as personal information? The result is somewhat of a paradox.
The blockchain – privacy paradox
Immutability
Records published to a blockchain cannot be deleted, but most modern privacy legislation grant individuals a “right to be forgotten”. How can an individual or data subject exercise their right to be forgotten when the information recorded on a blockchain’s ledger is permanent?
Continue reading: https://www.dentons.com/en/insights/articles/2022/june/9/the-privacy-paradox-in-blockchain-best-practices-for-data-management-in-crypto

Whether you work in the tech industry, a commercial sector that relies heavily upon it, or in a completely unrelated field, chances are you’ve heard the term, blockchain. From Twitter timelines to news reports to water-cooler conversations, it’s increasingly becoming a part of our global lexicon, right along with its cousins, cryptocurrency (crypto) and bitcoin.
If this emerging area of technology still seems a bit fuzzy and vague to you, take heart – you are not alone. But one thing is clear: blockchain technology is materially changing the tech industry. It is creating new opportunities – including increased demand for technical talent – based on new ways of doing business and how we transact, invest, store, secure, share and leverage digital data.
To get you up to speed, here’s a primer on how blockchain technology works and how it’s transforming the tech space.
What is a blockchain?
A blockchain in its simplest form is a distributed or decentralized database in which a set of data (a digital ledger of transactions) is stored among a network of computers making it more secure and difficult to hack or alter. In turn, this enables people to use the data to transact with one another more securely, without a third party, like a bank or government, being involved to control the transactions.
Continue reading: https://www.bizjournals.com/jacksonville/news/2022/06/09/how-blockchain-technology-is-changing-the-tech.html

With the changing workplace dynamics in the post-Covid world, India Inc stands at an inflection point in its ability to not only sustain but drive women careers in Tech. Large technology companies are making substantial progress in increasing female workforce representation. However, a lot can be done further. The proportion of overall women workforce witnessed an increase to 32.9% in 2022, from 30.8% in 2019 and even the role of women in technical roles have grown to 25% in 2022 from 22.4 % in 2019. While we can take satisfaction from this increased participation, a lot can be done towards ensuring that women workforce is able to sustain in their tech careers.
There is still a large disparity between the percentages of women graduating with STEM degrees and their representation in the tech workforce. While nearly half of the total STEM graduates are women, only 14 percent of scientists, engineers, and technologists in research development institutions and universities are women. While many graduates start a career in tech, sadly a larger percentage of women graduates have not been able to sustain their careers. Organizations can play an important role in driving multiple initiatives that could help women workforce. 
Upskilling and return ship programs
The post-pandemic technology acceleration has led to a demand of wide variety of technology focused skills in India. Upskilling has become the need of the hour for the young workforce and is the pre-requisite for empowering women to re-join the workforce. The World Economic Forum (WEF) and a growing number of analysts and think tanks have called for a “reskilling revolution” that can transform the future of work for women. Reskilling existing employees and those determined to return to work after a career break, is crucial in improving women representation in tech. Offering internships with an objective to reskill women with supporting factors such as transportation, subsidized food, and day-care reimbursements will be encouraging/motivational for women to return to work. Though such programs have seen limited success, whatever impact organizations can make go a long way in supporting careers for women who would like to re-join.
Continue reading: https://timesofindia.indiatimes.com/blogs/voices/sustaining-women-careers-in-tech/

When Sheryl Sandberg said this week that she was resigning as chief operating officer of Meta, she also reflected on her legacy as a woman in tech. “I’m especially proud that this is a company where many, many exceptional women and people from diverse backgrounds have risen through our ranks and become leaders — both in our company and in leadership roles elsewhere,” she wrote in an announcement posted on her Facebook and Instagram pages.
Yet even as Sandberg lauded the progress of women at Meta, the broader reality for female leaders at the top of the tech industry has been far more disappointing. And with her exit this fall, Silicon Valley is losing one of its most visible and outspoken female executives, leaving few — some would say zero — similar peers in her wake. Sandberg, 52, was part of a cohort of women at major tech companies who made keynote speeches, rose to the level of founders like Larry Page and Mark Zuckerberg, and had a seat at the table at high-powered business gatherings like the Allen & Co. conference in Sun Valley, Idaho. But over the years, many of these women — including Marissa Mayer of Yahoo, Meg Whitman of Hewlett Packard Enterprise and Ginni Rometty of IBM — have departed, often with their reputations in tatters. More broadly, women have not made notable gains in recent years in the highest echelons of Alphabet, Apple, Amazon, Meta and other tech giants where the corridors of power continue to be dominated by men. The industry’s record on female leadership trails that of other industries, even as tech exerts more influence in the global economy and in people’s lives. “The CEO is the face of the company,” and in the tech industry, “somehow collectively the world seems to want the face to be a white man,” said Jenny Lefcourt, a founder of All Raise, a nonprofit focused on advancing gender and racial equality, and an investor at Freestyle Capital.
Continue reading: https://www.forbesindia.com/article/news/what-sheryl-sandbergs-exit-reveals-about-womens-progress-in-tech/77115/1

The 100th day of Russia’s war in Ukraine was Friday, June 3rd. During this time, Ukrainian women, especially those in tech, have been the major force behind keeping the country’s economy alive, as many men are serving in the army. Women also face different, but still difficult, situations such as deciding whether to stay in Ukraine or leave with their children, and how to make enough money for their family, and so on.
Softjourn is a female-led, US-based tech company with an office and large employee presence in Ukraine. They recently launched a #SupportUkraineTech campaign to galvanize the global tech community to support Ukraine’s immense IT industry. The IT industry is Ukraine’s 3rd-largest export. Women Love Tech recently talked to Emmy Gengler – CEO & Founder of Softjourn – who gave us a unique perspective of Ukraine’s female tech community and how it has been stepping up much like women did during WW2.
“Softjourn is a women-led, US-based tech company with the largest R&D presence in Ukraine,” said Emmy, who is based in the US, but travels frequently between offices in Poland and Ukraine. “We’re a full-cycle consulting and software development company, with expert product teams experienced in Finance and Media and Entertainment, with a special emphasis on Ticketing.
“When the war started, we were not sure what reaction to expect from our clients, but we have been overwhelmed with the support they have shown to our team members in Ukraine. We’ve heard from a variety of clients that they have been impressed with the professionalism and work ethic of the teams, even with the war.
“We’ve had current clients sign additional projects with us, which has provided more revenue growth during these months over the past years. Additionally, we’ve received 300% year-over-year client signings since the war; this may be due to companies wanting to partner with Ukraine R&D teams to support Ukraine’s economy.”
Softjourn continues to support Ukraine, and its Ukrainian workforce has grown by 10% since the war started.
Continue reading: https://womenlovetech.com/meet-ukraine-women-in-tech-making-a-difference/

AI has fueled efficiencies across industries for years. It's old news by now, but as I've said before, that's a good thing.
Conversations about AI sound much different today than they did 10 years ago. Instead of wondering whether AI will help businesses grow or increase bottom lines, the proliferation of the technology has pushed AI conversations in more meaningful and complex directions. One area I'm particularly interested in is data privacy and biases in AI models.
You might remember the Plaid class-action lawsuit or the racial bias in Twitter's image-cropping tool. One is an instance of an AI algorithm collecting unnecessary customer data, while the other is a case involving biased AI decision-making. Algorithms themselves often lead to biased AI-powered decisions. However, these biases—which are oftentimes unconscious—stem from the humans who develop and train the algorithms.
Still, there's no excuse for unethical AI practices. You won't change my mind on that.
What exactly is unethical AI?
When I received my iPhone X in 2017, I couldn't wait to try the facial recognition feature. However, after many failed attempts, Apple's authentication technology wouldn't work. As an Asian American, I feared the technology's inability to recognize my face was related to my race. It turns out I wasn't the only person with the issue—Apple was accused of failing to train its AI model with broad enough sample data to recognize and distinguish people of color.
While unintentional, this issue created a subpar experience that frustrated many iPhone users—myself included. Let's take this scenario a step further. What if we start to power medical diagnoses with AI? Or self-driving cars, which is already happening? The consequences of these oversights become much graver and potentially life-threatening.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/06/09/your-ai-practices-might-not-be-ethical/?sh=1a30750071d6

Once an idea only existing in sci-fi, artificial intelligence now plays a role in our daily lives. In fact, we expect it from our tech products. No one wants to reconfigure their entire tech suite every time a new update is launched. We need technology that can process code for us, solve problems independently, and learn from past mistakes so we have free time to focus on the big picture issues. 
That’s where AI comes in. It makes projects run smoother, data cleaner, and our lives easier. Around 37 percent of companies use AI to run their businesses, according to the tech research firm Gartner. That number should only grow in coming years, considering the number of companies using artificial intelligence jumped 270 percent from 2015 to 2019. 
AI is already a staple of the business world and helps thousands of companies compete in today’s evolving tech landscape. If your company hasn’t already adopted artificial intelligence, here the top 15 tools you can choose from.
15 AI Tools
SYMANTEC ENDPOINT PROTECTION
Specialty: Cybersecurity
Companies that conduct any aspect of their business online need to evaluate their cybersecurity. Symantec Endpoint Protection is one tool that secures digital assets with machine learning technology. As the program encounters different security threats, it can independently learn over time how to distinguish between good and malicious files. This alleviates the human responsibility of configuring software and running updates, because the platform’s AI interface can automatically download new updates and learn from each security threat to better combat malware, according to Symantec’s website.
Continue reading: https://builtin.com/artificial-intelligence/ai-tools

Whether you’re just stepping into Web3, or you’ve been busy paving roads for a while now, there’s no denying that it’s a vast and often confusing place to be.
“We are literally rethinking everything from the ground up,” Colette Grgic, Head of Startup Ecosystem, AU & NZ  for AWS said at the recent Startups in the Web3 World AWS Summit 2022. “Like the world that we’re building, the way that we’re transacting, how we’re building community, how we live, how we spend our time, where we spend our money.”
Grgic went on to emphasize the importance of building community for Web3 startups. “Building this community and this ecosystem is really what’s going to enable us to all progress faster together.”
The AWS community itself includes huge success stories like Ethereum’s leading NFT platform Immutable, blockchain-powered fintech Block Earner, and Illuvium, a video game developer built on the Ethereum blockchain with collectible NFTs across the DeFi (Decentralized Finance) metaverse.
Then there’s A Township Tale, Alta VR‘s persistent virtual world where players journey together and interact in real time. And VeVe, which allows users to purchase, sell and trade digital collectables in virtual showrooms… if you know, you know.
“This is really where we need to start thinking about how are we enabling the entire next decade of entrepreneurs to build in a completely different way than what’s ever been done before,” said Grgic.
Trusted access to data
Despite the diversity of their products, all of these startups have a very clear commonality, one that’s at the heart of Web3: they are each solving a customer problem that Web2 couldn’t solve in the way they want.
At the Summit, Brendan Myers, Seniors Solutions Architect, AWS, described it as giving “trusted, unfettered access” to data.
“This thing that customers care about is that they can have data that exists outside of a system and they can use it however they want,” Myers explained. “They can take it from one place to another and they don’t need to go through a single gatekeeper to access it.”
Continue reading: https://www.startupdaily.net/partner-content/web3-is-helping-startups-disrupt-existing-models-and-create-the-impossible/

What Is a Blockchain in Simple Terms?
A blockchain, at its most basic level, is a digital ledger of transactions stored on many different computers (called nodes) that are linked by a network. It is composed of a series of “blocks,” which are essentially digital baskets that can be filled with records of transactions. Once the transactions in a block are verified via a consensus between nodes in the network, that block is “closed” and added to the existing, unalterable, chronological chain of previous blocks.
Most often, blockchains are used to buy, sell, trade, and record the ownership of cryptocurrencies (like Bitcoin, Ethereum, and Solana) or other digital assets like NFTs. They can be used for other purposes as well, but we’ll get to those later on.
You can think of a blockchain sort of like a chain-of-custody record for a piece of evidence in an investigation. Every time the piece of evidence (or in the case of a blockchain, a digital asset like a Bitcoin or an NFT) changes hands, this transaction is recorded on an unalterable ledger. 
Whereas a chain of evidence log can be altered or forged, a blockchain cannot because there are many copies of it on many different networked computers that have to confer on the legitimacy of a transaction in order for it to be permanently inscribed on the blockchain in the first place.
The appeal of a blockchain is that it is a secure, unchangeable record of transactions that doesn’t depend on any central authority, like a bank. In other words, no one person, entity, or institution must be trusted or relied upon in order for the blockchain to remain safe and secure. 
Continue reading: https://www.thestreet.com/dictionary/b/blockchain

Today, everyone is concerned about cybersecurity, and they should be. Cyber-attacks are on the rise, yet whenever a new IT development appears related to blockchain, people inquire: How safe is blockchain technology? It is an effective tool for ensuring data integrity. But that doesn’t imply it’s completely secure. Before we get started, here is a list of the best blockchain books in 2022 for better understanding. You may have heard about the blockchain talent gap and started to ask what is a blockchain developer. But unfortunately, you find some blockchain implementation challenges and security issues. Don’t worry; we have all the answers. So let’s take a closer look at the issues that threaten blockchain security.
BLOCKCHAIN SECURITY ISSUES AND CHALLENGES IN 2022
Blockchain—also known as distributed ledger technology—and the cryptocurrencies it powers have experienced plenty of success and failure in their brief existence. And as its applications expand, blockchain security has become essential—not just for cryptocurrency investors.
Blockchain is a decentralized, distributed ledger that maintains a record of all transactions. To guarantee transaction trust it relies on consensus, decentralization, and cryptography. However, many blockchain security problems have already emerged due to bad technology applications.
The blockchain has grown in popularity over several years as the cryptocurrency markets have moved toward center stage. One reason for its rapid adoption is that blockchain was created to provide unrivaled security to digital data. Rather than cryptocurrencies, there are several blockchain use cases, such as blockchain gaming.
Continue reading: https://dataconomy.com/2022/06/blockchain-security-vulnerabilities-2022/

When law professor Douglas Branson published the book The Future of Tech is Female in 2018, he must have imagined that the global tech industry would soon be led by women.
Four years later, that future is evident in east Africa. For a long time, men dominated all senior and c-suite tech roles in the region. But as more women gained opportunities and skills, they’ve not only joined the tech industry in droves—in 2019, women made up 30% of people in tech in sub-Saharan Africa—they’re increasingly sitting in positions of power, creating tech products and services that impact more people.
Their impact on the sector has been hard to ignore. In Kenya, women-led medium-sized enterprises accounted for 48% of such businesses in 2021, which contribute around 20% to the country’s GDP.
Through leadership of local and Silicon Valley companies, women are brushing aside hurdles in tech. Meanwhile, organizations in every east African nation have popped up to mentor and train more girls who are becoming the next generation of startup founders.
Women are finally able to lead
A decade ago, most of the top tech jobs in east Africa were held by men. Now a series of new appointments is changing the narrative that women cannot handle complex technologies. 
Last week, Microsoft Africa Development Centre (ADC), Microsoft’s engineering hub in Nairobi with up to 1,000 employees, appointed Catherine Muraga as the new managing director. She was previously the head of engineering at Stanbic Bank Kenya.
Continue reading: https://qz.com/africa/2174952/how-women-in-east-africa-are-reshaping-tech/

In most organizations, artificial intelligence models are “black boxes,” where only data scientists understand what exactly AI does. That can create significant risk for organizations.
Large, sensitive datasets are often used to train AI models, creating privacy and data breach risks. The use of AI increases an organization’s threat vectors and broadens its attack surface. AI further creates new opportunities for benign mistakes that adversely affect model and business outcomes.
Risks that are not understood cannot be mitigated. A recent Gartner survey of chief information security officers reveals that most organizations have not considered the new security and business risks posed by AI or the new controls they must institute to mitigate those risks. AI demands new types of risk and security management measures and a framework for mitigation.
Here are the top five priorities that security and risk leaders should focus on to effectively manage AI risk and security within their organizations:
1. Capture the extent of AI exposure
Machine learning models are opaque to most users, and unlike normal software systems, their inner workings are often opaque to even the most skilled experts. Data scientists and model developers generally understand what their machine learning models are trying to do, but they cannot always decipher the internal structure or the algorithmic means by which the models process data.
This lack of understandability severely limits an organization’s ability to manage AI risk. The first step in AI risk management is to inventory all AI models used in the organization, whether they are a component of third-party software, developed in-house or accessed via software-as-a-service applications. This should include identifying interdependencies among various models. Then rank the models based on operational impact, with the idea that risk management controls can be applied over time based on the priorities identified.
Continue reading: https://siliconangle.com/2022/06/08/manage-artificial-intelligence-risk-security-focus-five-priorities/