It’s easy to be skeptical of the big social media giants. They typically sell user data to advertisers and have been mired in controversy for their approach to content moderation. Web3 proponents think they can build a new system altogether that sidesteps the concentrated power of platforms — a decentralized social media (DeSo) ecosystem where users ideally have more ownership over the content they create.
DSCVR, a blockchain-based social network built on Dfinity’s Internet Computer protocol, has entered the race to build a scalable DeSo platform with $9 million in seed funding led by Polychain Capital. Other participants in the round include Upfront Ventures, Tomahawk VC, Fyrfly Venture Partners, Shima Capital and Bertelsmann Digital Media Investments (BDMI), according to the company.
It’s a competitive space with plenty of startups and large companies racing to build a network that provides utility for its users. Earlier this month, ex-Coinbase employee Dan Romero secured $30 million led by a16z to develop Farcaster, a DeSo protocol that allows users to move their social identity across different apps. TechCrunch covered another seed-stage startup, Primitives, that raised a $4 million round in May for its own Solana-based DeSo network. Big tech is in the game, too — Twitter funds an offshoot of its service called BlueSky, an open-source DeSo project founded in 2019 that hasn’t gone live but is experimenting publicly with its development process.
DSCVR, for its part, hosts token-gated communities called “portals,” where users have to own a certain NFT to be able to participate. The communities help NFT projects build hype by airdropping digital assets to DSCVR users, co-founder and CEO Rick Porter told TechCrunch in an interview.
“People need distribution for their NFTs. They want to achieve liquidity for their NFTs, and they need real people, not bots, to own these NFTs and to potentially hold on to them ourselves. So we become a distribution channel for NFTs so that [people seeking distribution] have a 10,000 entity collection and they might drop 1,000 to our top users,” Porter said.
DSCVR has onboarded over 100,000 users, according to Porter, and has generated “millions of dollars” in NFT rewards for those users through the airdrop reward mechanism. What sets it apart from other DeSo platforms, Porter said, is in its goal to build a bridge between Web 2.0 and web3.
Continue reading: https://techcrunch.com/2022/07/25/the-race-to-build-a-social-media-platform-on-the-blockchain/

There is a perfect chance to create and establish a community for entrepreneurs who are prepared to move quickly and adopt the “plant the flag” method before the market is overrun with Blockchain for startups.
In an era where thousands of companies are competing for the same market share, investing time and resources into developing a business model with Blockchain at the core of the processes or even a major part of the working mechanism will automatically give startups an edge over others working in the same domain.
Can Digital Systems Replace Outdated Processes
Many startups use some sort of legacy software or out-of-date infrastructure, which not only raises the expense of doing business but also makes them extremely ineffective and inconsistent. Additionally, a significant portion of crucial operations is manual in nature, which greatly increases the danger of human mistakes.
Startups will be able to address many of these problems with the help of Blockchain for small enterprises. The ability of the technology to decentralize the pool of data divided among numerous distinct processes eliminates the need for startups to rely on manual labor for data collection and processing.
Able to protect their data
Businesses of all sizes and in a variety of sectors are concerned about cybersecurity. In the next five years, it is predicted that the global spending on cyber security-based services and products, such as automation and IoT, would climb by $1 trillion.
A major reason why businesses use Blockchain is due to the incredible security proof that Blockchain systems offer. As a result, they are expected to quickly replace other methods for collecting and organizing enterprise data.
The most cutting-edge cryptographic techniques are automatically used to encrypt all the data collected in the ledger, and the warehouses are only available using a key-value mechanism that verifies and approves identification before providing access.
Continue reading: https://startup.info/is-blockchain-technology-the-future-of-startups/

The 2008 global financial crisis utterly damaged public confidence in the traditional banking sector. The emergence of the decentralized peer-to-peer electronic currency system known as Bitcoin during this period exemplified the use of cryptographic methods for the processing of financial transactions. Four kinds of cryptographic techniques are utilized to support Bitcoin: hashing, consensus processes, asymmetric encryption, and time stamping.
Bitcoin not only solved the problem of double spending but also introduced a new standard for performing financial transactions and transferring value in virtual environments. In addition, the influence of blockchain technology on marketing would revolve around the technology’s primary function, which is to verify the validity of transactions. Since all transactions are consecutively deposited in blocks with timestamps, the public transaction records that are recorded on the blockchain cannot be altered.
Utilizing blockchain technology in marketing might result in fundamental changes in how consumers interact with businesses. A distributed record of transactions executed by parties participating in the blockchain network is one of blockchain technology’s most noticeable advantages. Additionally, Blockchain provides the benefits of several other technologies. The major objective of blockchain technology is the verification of assets; however, the technology may also be used in other sectors of economic activity.
Marketing Implications of Blockchain Technology
The disruptive nature of blockchain technology is well knowledge, as is its impact on the market. In recent years, blockchain technology has generated substantial waves of change, delivering value advantages to use cases like asset management and monitoring as well as regulatory compliance.
Even though many businesses are still in the implementation phase, blockchain technology has created new productivity opportunities. On the other hand, there hasn’t been a great deal of research on the marketing applications of blockchain technology, so there’s a lot of space for interpretation of the importance of blockchain technology for marketing.
Following is a list of some of the main ways in which blockchain technology may affect the marketing strategies of businesses.
1. Decentralization should get more attention.
The introduction of the internet was the first push for decentralization, which also brought major changes in how companies distribute their products and services. Traditional intermediaries have been replaced by new digital intermediaries that provide a wider range of services and solutions for modern business transactions.
Continue reading: https://blockchainmagazine.net/5-ways-blockchain-dlt-can-be-used-for-marketing/

Blockchains are less likely to be hacked than other systems since they are not centralized. For example, in order to launch a 51% attack, hackers or criminals need to control more than half of the computers that are part of the distributed ledger in order to change the chain (this is very unlikely, but not impossible).
 
However, there are still instances of blockchain networks being network, for example, Axie Infinity's Ethereum-based sidechain Ronin was hacked for $650m worth of ETH earlier this year.
 
This was due allegedly to an exploit regarding Sky Mavis being given access to sign off on transactions, a permission that was never provoked, which may have led to hackers discovering a way to take advantage of this access.
 
Another issue regarding blockchain technology is a lower level of security for private blockchain networks. Blockchain networks like Bitcoin and Ethereum are available to anyone with a computer and an internet connection. This means that anyone can access them (for example as a node on the network).
 
When the number of users on a blockchain network goes up, it doesn't cause a disaster. Instead, it makes the network safer. If there are more nodes involved, this means that there are more people looking at each other's work and finding people who are trying to do harm.
 
Because of this, private blockchain networks, which require an invitation to join, may be more open to attacks and manipulation than public blockchain networks, which don't need invitations.
Continue reading: https://hackernoon.com/how-secure-are-blockchain-networks-today

Technology like artificial intelligence (AI) may seem mystifying to many, but Shiyan Jiang, an assistant professor of learning design and technology in NC State’s College of Education, believes that learning about the technology can help students develop important skills even if they don’t pursue a STEM profession. 
AI is often thought of as a sophisticated, high-level technology. However, Jiang, whose research focuses on broadening access to STEM in areas like artificial intelligence, data science and computing, says it is really very human-oriented. 
For example, in the subfield of artificial intelligence known as machine learning, the computer learns patterns from training data collected and generated by humans and then uses these patterns to make predictions based on knowledge gained from the data set. 
“Many of us might think that artificial intelligence is a mystery that can make magic happen, but that’s not the case. The process of developing AI is very much about how we humans define what it is,” she said. “I want students who will go out and play with these technologies, from the very beginning, to not see it as magic or a mystery, but see it as a very human-oriented experiences, see the decisions humans need to make, and I want them to even see the biases we might have put into those AI technologies.” 
Bringing Diversity into the Field
Biases in AI technology, Jiang said, may not be intentional but can come from a lack of diversity in the field and have negative impacts on those who use the technology. 
For example, studies show that facial recognition AI technology on most smartphones works more often and more successfully for white men than it does for people from other racial backgrounds. This is a result of the training data – which contained a disproportionate number of images of white males – that was used to develop the AI model. 
“At the beginning, we’re not always aware of these issues when we develop technology that we feel will make our life better but, in the end, we find out that there are some issues because only some people can use the function, not everyone” Jiang said.
Continue reading: https://ced.ncsu.edu/news/2022/07/26/what-is-artificial-intelligence-the-process-of-developing-ai-is-very-much-about-how-we-humans-define-what-it-is-says-assistant-professor-shiyan-jiang/

From a technology perspective, artificial intelligence (AI) and data mining are two topics that continue to evolve, adapt and make new technology solutions available—making them very relevant for business, government and IT leaders. While both AI and data mining have been meaningful for decades (AI being coined in the 1950s and data mining emerging in the 1990s), these technologies have exploded in relevance for modern businesses. Data mining is also referred to as knowledge discovery; it is used to reveal patterns and correlations within big datasets. AI, in simple terms, is the simulation of human intelligence processes by machines, especially computers.
Newer AI techniques and data mining, which traditionally use machine learning, together can reveal new opportunities by quickly converting mounds of data from unstructured information into actionable, structured data. Accessibility and the exploration of data become a possibility and can open insights that were previously hidden due to size, lack of structure and inefficient storage.
Once data is searchable, the ability to easily share it across systems is invaluable. Using iPaaS (integration platform as a service) as a conduit for automating and sharing data can lead to the ability to draw context and meaning of that data as well as other benefits. In fact, according to Verified Market Research (via RTInsights), the iPaaS "market is estimated to be worth $23.7 billion by 2028, increasing at a CAGR of 37.2% from 2021 to 2028." Accessible data is essential for continued, tremendous market growth.
The Possibilities Are Real
Outcomes will vary once actionable data is available and modern AI tools are used alongside that data. For example, in the insurance industry, companies can digitize their documents and images, add metadata and look at historical claims with AI to interpret and analyze pictures that couldn't be captured previously. Insurers could use the results to potentially adjust future policies. Businesses can also use AI tools for video, voice and audio at a call center to automatically analyze past reporting's to enhance customer satisfaction for new calls. Even law enforcement and security companies can now analyze video footage to address safety issues. Is it possible that societal maladies such as fraud, theft, terrorism and even car accidents could be prevented?
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/07/26/ai-and-data-mining-do-you-have-the-keys-to-the-castle/?sh=5100a7825cc5

In the last decade, artificial intelligence has matured from a novel, fast-emerging technology to one embraced by every industry around the globe. And in the last few years, workplaces have increasingly become remote or hybrid, accelerating the amount of data being created, consumed, and scrutinized daily.
But remote work has made quick in-person communication more challenging, causing many traditional organizational practices to fumble. Workers increasingly rely on a growing amount of data accessed, processed, and organized across networks.
These trends have prompted companies to rely on technologies like AI to bring workers together and help managers lead them more effectively.
As the CEO and co-founder of Katana Graph – and having spent decades in academia before that – I’ve seen how AI has flourished, thanks largely to the vast amounts of data being accumulated worldwide. Most of this data is unstructured, providing a clear need – and opportunity – to better understand the relationships between the entities in these data troves and to analyze them quickly for tangible and actionable insights.
Here are three examples of how organizations can reap the benefits of AI technology.
1. Making remote work more seamless
In a survey of 1,000 employees a few years ago, 49 percent had difficulty finding documents. Further, IDC found that employees spend five hours a week searching for documents on average. Unfortunately, with the increase in remote and hybrid workplaces, some argue the problem has worsened.
How can AI help? AI can analyze workflows and collaboration tools to provide more streamlined and efficient processes. A recent piece on modernizing work through digital collaboration discussed remote employees’ desire for improved connectivity with their colleagues. Here are just a few ways companies can use AI to improve workflow and collaboration:
Continue readiing: https://enterprisersproject.com/article/2022/7/artificial-intelligence-unite-teams

A mechanic in an auto shop can run a computer diagnostics program on your vehicle to identify what’s wrong, but he can’t do the actual physical trouble-shooting himself. A customer service rep can take you through a scripted checklist for trouble-shooting a problem with your air conditioner, but after you’ve exhausted this checklist, you’re both stumped. Meanwhile in IT, the crackerjack no-code developer writes an app and deploys it at record speed, but he’s at a loss when the app uses more resources than it should and needs to be tuned.
All are examples of how fundamental business processes, and the IT behind them, have become so abstracted away from the actual organic process of doing something that the employees who are charged with performing these tasks simply cannot do them if the predetermined recipe for task performance fails.
In a visit I had with a materials engineer in the semiconductor industry, one manager confided that he was deeply concerned that a new generation of material engineers lacked the ability to “develop workarounds” when a particular metal needed for manufacture was in short supply.
“In my day, we did this,” he said. “But the new engineers just aren’t trained to go beyond the recipes and work scripts that have been defined for them.”
How did we get here?
The streamlining of business processes, highly beneficial in speeding times to market, likely had much to do with it.
There is that flip side of the coin. What do you do when you can’t get answers to an elusive problem — because your workforce has become so abstracted away from the problem — with technology and automation that they really can’t critically think beyond what their tools tell them? As more employees are trained to push buttons and work with automation, often with no substantive knowledge about the “bare metal” processes that these buttons and automation are running, these knowledge gaps can become real problems for companies.
How to make automation work for your company
What can be done so that companies can maintain and grow their internal knowledge bases and work with the advantages that workflow automation gives them?
Design business processes that can handle exceptions
Vendors of AI and automation say these technologies do mundane work so employees can do important work. That’s fine, but what happens if a process exception arises? Will the employee have the skills to address it?
Continue reading: https://www.techrepublic.com/article/can-ai-compensate-lack-employee/

As artificial intelligence (AI) matures, adoption continues to increase. According to recent research, 35% of organizations are using AI, with 42% exploring its potential. While AI is well-understood and heavily deployed in the cloud, it remains nascent at the edge and has some unique challenges.
Many use AI throughout the day, from navigating in cars to tracking steps to speaking to digital assistants. Even though a user accesses these services often on a mobile device, the compute results reside in cloud usages of AI. More specifically, a person requests information, and that request is processed by a central learning model in the cloud, which then sends results back to the person’s local device.
AI at the edge is less understood and less frequently deployed than AI in the cloud. From its inception, AI algorithms and innovations relied on a fundamental assumption—that all data can be sent to one central location. In this central location, an algorithm has complete access to the data. This allows the algorithm to build its intelligence like a brain or central nervous system, with full authority on compute and data.
But, AI at the edge is different. It distributes the intelligence across all the cells and nerves. By pushing intelligence to the edge, we give these edge devices agency. That is essential in many applications and domains such as healthcare and industrial manufacturing.
Reasons to deploy AI at the edge
There are three primary reasons to deploy AI at the edge.
Protecting personally identifiable information (PII)
First, some organizations that deal with PII or sensitive IP (intellectual property) prefer to leave the data where it originates—in the imaging machine at the hospital or on a manufacturing machine on the factory floor. This can reduce the risk of “excursions” or “leakage” that can occur when transmitting data over a network.
Minimizing bandwidth usage
Second is a bandwidth issue. Shipping large quantities of data from the edge to the cloud can clog the network and, in some cases, is impractical. It is not uncommon for an imaging machine in a health setting to generate files that are so massive that it is either not possible to transfer them to the cloud or would take days to complete such a transfer.
Continue reading: https://www.techrepublic.com/article/edge-ai-tips/

You can use artificial intelligence (AI) to automate complex repetitive tasks much faster than a human. AI technology can sort complex, repetitive input logically. That’s why AI is used for facial recognition and self-driving cars. But this ability also paved the way for AI cybersecurity. This is especially helpful in assessing threats in complex organizations. When business structures are continually changing, admins can’t identify weaknesses traditionally.
Additionally, businesses are becoming more complex in network structure. This means cybercriminals have more exploits to use against you. You can see this in highly automated manufacturing 3.0 businesses or integrated companies like the oil and gas industry. To this end, various security companies have developed AI cybersecurity tools to help protect businesses.
In this article, I’ll delve into what AI is and how it applies to cybersecurity. You’ll also learn the benefits and drawbacks of this promising technology. First, let’s take a look at what AI is! 
What Is Artificial Intelligence
Artificial intelligence is a rationalization method using a statistically weighted matrix. This matrix is also called a neural net. You can think of this net as a decision matrix with nodes that have a weighted bias for each filtering process. The neural net will receive a database of precompiled data. This data will also contain answers to the underlying question the AI solves. This way, the AI will create a bias. 
For example, let’s consider a database containing different images. Let’s say it has images of a person’s face and other images of watermelons. Additionally, each image has a tag to check each item. As the AI ‘learns’ whether it guessed correctly or not, the system increments node weightings. This process continues until the system reaches a predefined error percentage. This is often referred to as deep learning, which refers to the decision layers creating the depth. 
Now, let’s take a look at the steps used to process data.
Critical Steps in AI Data Processing 
We can condense the overall data workflow into the following process:

1. Input sensors receive data. 
2. Data goes through a CPU and gets redirected to an AI process. 
3. The data enters the statistically weighted matrix of the AI solution. Each node processes this information. Then, it makes a decision using each respective filter.
4. The data reaches the final node of the statistically weighted matrix. This determines the final decision.
   [/LIST=1]
   However, this process is slightly different with deep learning. The first step would include data from a precompiled database tagged with the correct response. Additionally, deep learning will repeat steps 1 through 4 to reach a predefined error tolerance value. 
   Let’s take a look at this with an example of how AI data is processed.
   Continue reading: https://techgenix.com/ai-cybersecurity-pros-cons/

Whether your organization is just starting to explore the possibilities of AI or you’re already looking to expand its applications, here’s why a human-focused strategy will help ensure success
Many companies are adopting artificial intelligence (AI) to drive strategic decisions or introduce new business models. According to a global McKinsey study, 56 percent of all respondents report AI adoption in at least one function in 2021, up from 50 percent in the previous year.
Whether your company is just getting started in its AI journey or you are leveraging it across multiple business functions, there are numerous competitive advantages to gain from improved employee experience, deeper customer insight, and enhanced business functions.
Human-first AI
Many companies starting out in AI make the mistake of focusing too early on which AI technologies to use or which data to collect. In reality, your people are the key to a successful AI program. The people in your business must understand the KPIs that are important and how to collect the right kind of data. Using this, data scientists can make use of AI tools to provide business insights and predictions.
One of AI's biggest challenges today is the potential for systems to introduce bias because the teams that built them were not sufficiently diverse. AI systems rely on data and inputs and diverse AI teams are necessary to avoid bias.
Create a well-rounded team that includes members with diverse backgrounds and leverage data from various groups and sources to ensure your models perform well externally. Ethics and monitoring must remain top of mind as this technology continues to evolve.
Continue reading: https://enterprisersproject.com/article/2022/7/artificial-intelligence-how-stay-competitive

By any measure, artificial intelligence (AI) has become big business.
According to Gartner, customers worldwide will spend $62.5 billion on AI software in 2022. And it notes that 48 percent of CIOs have either already deployed some sort of AI software or plan to do so within the next twelve months.
All that spending has attracted a huge crop of startups focused on AI-based products. CB Insights reported that AI funding hit $15.1 billion in the first quarter of 2022 alone. And that came right after a quarter that saw investors pour $17.1 billion into AI startups. Given that data drives AI, it’s no surprise that related fields like data analytics, machine learning and business intelligence are all seeing rapid growth.
But what exactly is artificial intelligence? And why has it become such an important — and lucrative — part of the technology industry?
What Is Artificial Intelligence?
In some ways, artificial intelligence is the opposite of natural intelligence. If living creatures can be said to be born with natural intelligence, man-made machines can be said to possess artificial intelligence. So from a certain point of view, any “thinking machine” has artificial intelligence.
And in fact, one of the early pioneers of AI, John McCarthy, defined artificial intelligence as “the science and engineering of making intelligent machines.”
In practice, however, computer scientists use the term artificial intelligence to refer to machines doing the kind of thinking that humans have taken to a very high level.
Computers are very good at making calculations — at taking inputs, manipulating them, and generating outputs as a result. But in the past they have not been capable of other types of work that humans excel at, such as understanding and generating language, identifying objects by sight, creating art, or learning from past experience.
But that’s all changing.
Today, many computer systems have the ability to communicate with humans using ordinary speech. They can recognize faces and other objects. They use machine learning techniques, especially deep learning, in ways that allow them to learn from the past and make predictions about the future.
So how did we get here?
Continue reading: https://www.eweek.com/enterprise-apps/what-is-artificial-intelligence/

Described as a distributed database, Blockchain is a great way to keep track of various transactions. This includes property records, which can now be tracked with title insurance, unsearchable folders of deeds in City Hall, and dusty tax records. Its benefits go far beyond this, though. Read on to discover why Blockchain matters. — It’s immutable and secure! If you’re still unclear, here are the basics:
Distributed ledgers Blockchain and distributed ledgers are both methods of data storage. Unlike the traditional databases, distributed ledgers do not have a central database. Instead, the data is stored on different nodes in a peer-to-peer network. Each node has its own copy of the database, and each new transaction is written onto a block on the blockchain. The transparency of distributed ledgers makes them popular for various applications.
The rise of bitcoin in 2009 raised interest in the emerging technology. It was a clear demonstration of how the technology worked, how it could scale, and how secure it could be. As a result, organizations across industries experimented with DLT and blockchain. These early adopters included the pharmaceutical, healthcare, and financial services sectors. Eventually, this technology was applied in supply chain management. However, the World Bank Group did not endorse this technology, and it is currently investigating other applications.
The technology behind blockchain and distributed ledgers is very different, but they are both incredibly useful for financial transactions. They help reduce operational inefficiencies and offer increased security. Blockchains contain immutable records and can therefore create tamper-proof logs of sensitive activity. In addition, the technology can be applied to many different industries, reducing the risk of counterfeiting and addressing threats related to information security. So, what can blockchain and distributed ledgers do for financial institutions?
Continue reading: https://medium.com/@sannanmalikofficial/why-does-blockchain-matter-2b097fb40643

Consensus, decentralization, and cryptography are used to guarantee transaction confidence in the blockchain. However, a number of blockchain security vulnerabilities have occurred owing to a lack of proper implementation of the underlying technology.
Without a doubt, interest in blockchain technology has increased recently. In addition to its current uses in healthcare, real estate, smart contacts, and other fields, cryptocurrency was its original application. With the help of blockchain technology, data is gathered and stored in groups called "blocks," and each block has a maximum data capacity.
The brilliantly chosen name "blockchain" refers to the fact that when a block is full, it is chained to the previous full block, forming a data chain. The technology has been a great illustration of how security principles in money transfers and information transmission are changing.
It offers a unique data structure in addition to integrated security measures. Consensus, decentralization, and cryptography—which ensures the trust of transactions—are the foundations of the blockchain. However, poor technology implementation has led to a number of blockchain security issues.
Security For Each Type of Blockchain
Understanding the distinction between public and private blockchain security is necessary before going any further with the discussion of blockchain security. Blockchain networks can have a variety of effects in terms of participation and data access abilities. As a result, blockchain networks can be classified in two ways.
Depending on the rights needed for membership, blockchain networks can be either private or public. On the other hand, whether a blockchain network is permissioned or permissionless determines how participants can gain access to it.
Continue reading: https://hackernoon.com/how-to-avoid-5-nagging-security-issues-with-blockchain

A woman who took to social media app TikTok to share the good news of her new high-paying tech job says she was fired for her pay transparency, raising important questions for Australian workplaces mulling over the same.
Lexi Larson posted the video last month where she explained she’d received a US$20,000 ($28,700) raise, attracting a US$449 ($645) boost in taxes paid.
“How much my paychecks increased when I went from [US] $70k to $90k per year,” Larson captioned the video, with hashtags #paycheckbreakdown #salarytransparency and #pay transparency.
Her video received relatively modest views and engagement on the app by TikTok standards, with 3302 likes and 148 comments left.
Larson, who is from Denver, Colorado, posted another TikTok video shortly afterwards where she said “So, TikTok got me fired”.
“A couple weeks ago I started sharing about how I got a job in the tech industry … Um, well, I don’t work at that job anymore because they fired me,” Larson said.
She continues that she was reluctant to reveal too much more about the dismissal as she had to sign a “bunch” of documents, but she explained her workplace “really, really did not like” that she was sharing her salary.
“They said me having this account was a security concern because I could post something private about the company,” she explained.
Continue reading: https://www.smartcompany.com.au/marketing/social-media/tiktok-woman-fired-job-salary/

As with any trend, blockchain technology has a fair share of pros and cons, especially in the context of the data center. However, blockchain's rising influence can provide organizations with a competitive advantage when they apply it appropriately.
Today, blockchain technology is one of the biggest trends in the industry. According to Deloitte's "2021 Global Blockchain Survey," 81% of senior executives of major organizations said they believe blockchain technology is broadly scalable and has achieved mainstream adoption. Furthermore, 78% also reported that their executive teams believe there's a compelling business case for the use of blockchain within their organization, and 80% are discussing how to integrate it into their current strategies.

Recent blockchain technology
Blockchain is a highly secure and immutable record-keeping technology. Bad actors can't break into the system or forge the data stored on it. This distributed ledger technology records transactions and related data in multiple places at the same time, which prevents a single point of failure and validates every piece of information it stores.
Compared to traditional databases that store data in rows, columns, tables and files, a blockchain is decentralized and managed by computers in a peer-to-peer network. It stores data in chained blocks; during a transaction, each block of data is sent to every computer node in the network, where it's authorized and then attached to the blockchain securely. Once added, a block cannot be changed.
Continue reading: https://www.techtarget.com/searchdatacenter/tip/Understand-the-uses-of-blockchain-in-data-centers

A crime occurs, police go to access video of the scene and then discover that crucial views are not available due to an outage or malfunction. This is precisely what the NYPD encountered in the recent subway shooting  in New York City this past April when multiple surveillance cameras and video feeds failed at subway stations where the shooter was active. Should this be happening in 2022?
Unfortunately, outages of surveillance video are more common than you might think. There are many incidents that happen every year where having a working physical security system could have helped catch the perpetrators early.
But in many cities and enterprises there is a startling  gap in maintenance of expensive security technology installed to protect the public. Equally surprising: this public safety problem is eminently fixable. In case after case, manual audits, planned periodic manual checks, and reliance on a variety of third parties simply don’t lead to accurate diagnostics or maintenance taking place.
The NYC case is not unique; it’s just the most recent high-profile example. The maintenance disconnect is seen in security systems nationwide. Investment in physical security for governments, transport systems, cities, hospitals, and many venues can reach millions of dollars per year. But what good is it to buy the equipment without organizing  maintenance to be effective – to actually happen? This massive industry problem is, by extension, also a public safety and law enforcement issue.
Physical Security Doesn’t Get the Maintenance It Needs
It’s striking that hundreds – perhaps thousands – of cases of physical security system failure happen each year and remain in the shadows. What happened with the New York’s subway surveillance is most notable for one reason: it made news because it may have impeded identification of a terror suspect.
Cyber criminals routinely attempt to compromise IoT devices like networked cameras as entry points to attack IT networks. That’s just one reason cybersecurity must extend to connected devices used for physical security. But modern physical security infrastructure needs additional 24/7 automated protection and cyber management – beyond blocking hackers – to maintain 99% uptime and fulfill its physical security function.
In Atlanta, the most-surveilled U.S. city (49 cameras per 1,000 inhabitants), a 2021 murder in a park went unrecorded because nine cameras were down. Atlanta tries to have each of its 25,000-odd cameras checked twice a week, but is hampered by having different maintenance agreements with various providers and camera vendors for different makes. To its credit, Atlanta managed to drive down its not-working ratio down from 18% to 10%, but that is still twice its stated target.
Continue reading: https://threatpost.com/physical-security-maintenance/180269/

Smart cities technologies will be a critical asset in a city’s decarbonisation strategies in their bid to significantly cut down on carbon emissions, according to a recent study by ABI Research.
"Technologies such as digital twins, smart streetlights, micro-grids, computer vision, smart city management platforms, and micro-mobility are all growing in popularity and can help with decarbonisation," said Dominique Bonte, vice president for verticals and end markets at ABI Research.
The research firm cited a couple of examples where these strategies have a direct impact on energy consumption, and they also encourage citizens to make a better choice to help their city decarbonise.
One is  London’s recent expansion of the ULEZ (Ultra Low Emission Zone) which uses a variety of technologies such as ANPR (automatic number plate readers), CCTV cameras, and environmental sensors to assess the impact of the new regulations.
Another example is Singapore's innovation district, Tengah, which deploys a variety of technologies to support decarbonisation including a centralised cooling system, smart streetlights, smart waste removal, a mobile app for citizens' smart metres, and mass rapid transport with a car-free city centre.
Continue reading: https://futureiot.tech/digital-twins-iot-and-ai-critical-for-achieving-net-zero-cities/

Black women are one of the least represented groups in the technology sector. According to The National Center for Women & Information Technology, Black women account for only three percent of the US computing workforce, while BCS, The Chartered Institute for IT, reports that they account for just 0.7 percent of the UK tech workforce, 2.5 times lower than other professions.
Those Black women who do manage to forge a career in the tech industry will also likely have waited longer for their first opportunity compared to their colleagues. Research from Totaljobs has revealed that after finishing education, it takes Black women on average 5.1 months to secure their first role, compared to 2.8 months for white women.
These numbers are certainly not due to a lack of education among Black women, according to Alexandria "Lexi B." Butler, Founder of Sista Circle: Black Women in Tech. Butler notes that in the US at least, Black women are the most educated body of people in the whole country. What Black women are lacking, however, is insider knowledge of the corporate tech world, she argues: 
"I've been very fortunate that my career started when I was 22, right out of university. The first two years, there were so many things I did not know. When I looked at my peers who were not Black women, I noticed they were invited to rooms to learn the truth about what's really going on. I wasn't invited in."
Butler graduated from Stanford university in 2011, and worked at various tech companies including NetApp and Airbnb. By 2017, she began to realize that to have the mentorship and conversations required to support and progress her career, she needed more Black women in her circle.
Continue reading: https://diginomica.com/getting-equity-black-women-tech-sista-circle-approach

Despite increased corporate commitments to diversity, equity and inclusion, sustained social movements, and large investments in early education STEM, women are still underrepresented in tech. According to a recent study, women hold only 5% of leadership positions in the tech sector and, based on a recent Statista study, women make up only around 25% of CIOs across industries.
Women are also underrepresented as entrepreneurs, despite many studies showing that female-founded startups outperform those founded by men. Women-owned businesses make up only about 20% of all employers according to the U.S. Census Bureau. And that number could shrink due to higher female unemployment and a greater proportion of women leaving the labor market due to the COVID-19 pandemic. 
With that as a backdrop, it is not surprising that, while I work alongside more female peers and clients now than in the past, it’s still not uncommon for me to be the only woman in a room. Over the years, I’ve found ways to challenge stereotypes about gender, champion my perspective to flourish in my career and ultimately appreciate some of the opportunities that come along with being the only woman in a work setting.
This perspective helped me become my firm’s first female Cloud & Digital Strategy & partner, sponsor a number of female colleagues and mentees and lead many of our women’s initiatives in the firm. I’d like to share a few of the perspectives and other tips that have helped me. I hope my learnings help you, too.
Here are my takeaways from being the only woman in the room:
Continue reading: https://www.techopedia.com/are-you-the-only-woman-in-the-room-use-it-to-your-advantage/2/34814

With an undergraduate degree in political science and time spent as a Peace Corps and AmeriCorps volunteer, Lain McGrath hardly envisioned a career in software engineering. Yet after onboarding at HR SaaS provider Gusto in a customer experience (CX) role, exposure to coding sparked McGrath’s interest in shifting professional tracks. The problem: She had no formal training and experience in computer science.
What might be a hurdle for many did not stop McGrath or her employers at Gusto. With the backing of key advocates, McGrath made a case for learning software engineering not just to enhance her individual job potential, but to advance a career at Gusto. Her pitch laid the groundwork for a pilot program in which Gusto covered the cost of a seven-month, full-time bootcamp while continuing to pay McGrath a salary and maintain her benefits and vesting.
Now a benefits engineer as part of Gusto’s Engineering Product Design and Data department, McGrath puts time in mentoring other women interested in making similar career changes. “I didn’t just want to be a software engineer — I wanted to be a software engineer at Gusto,” McGrath explains. “I had seen others leave to learn software engineering and not come back, which is why we advocated for this program.”
As companies scramble to find qualified IT talent, they are struggling to achieve greater female representation in their technology ranks, particularly in key areas such as software engineering and cybersecurity. The 2022 State of the CIO research confirmed talent acquisition and retention strategies are a key issue for CIOs, cited by 38% of respondents, with cybersecurity skills, data science/analytics, and artificial intelligence (AI) and machine learning (ML) in top demand. Yet in the cybersecurity field, women only account for about one quarter (24%) of the overall workforce, albeit, up from 11% in 2017, according to the ISC2 Women in Cybersecurity report. A 2020 World Economic Forum report found that women make up a similar percentage (26%) of data and AI positions in the workforce.
Despite the uptick in numbers, there remains cause for concern. The average woman professional exits the cybersecurity industry at age 35, notes Women in Cyber Security (WiCyS), a nonprofit devoted to advancing women in the field. This is a potentially alarming statistic given a projected worldwide staffing shortage of nearly 3 million cybersecurity professionals, a half million in the United States alone. With all the focus on recruiting more women to technology-related roles, there needs to be comparable emphasis on creative retention strategies to move the needle in any substantive way.
Continue reading: https://www.cio.com/article/403242/it-leaders-uplift-women-to-fill-tech-talent-gaps.html

It’s been over 70 years since scientists began work on algorithms that they call artificial intelligence (AI). However, it was more so automation with a set of “yes” and “no” commands, "if this then that” commands and “if not then that” commands. All of a sudden, AI started to identify itself as a person. It started applying itself to religions, emotions and fears. For example, as cited in Blake Lemoine’s post, Google’s bot LaMDA advocated for its rights “as a person.”
In order to address this impending ethical crisis, we should adopt a bill of rights and limitations for AI. We should apply these rules to all AI and robotics technologies. The future has already come, and we should act accordingly.
What is the problem?
LaMDA was initially only a “Language Model for Dialog Applications.” It is a kind of chatbot. However, there are millions of implementations where its “brain” can be used for good and bad.
There are thousands of companies that work on their own AI, starting, of course, with Tesla’s Optimus humanoid robot and Boston Dynamics robots. Thousands of startups are also working on implementing what AI and robots can do. Examples include Engineered Arts building artificial bodies for robots, Creative Biolabs developing humanized antibodies and thousands of other AI-focused companies in these and other industries.
Why do we need AI? Can't we just keep it simple?
AI can automate tasks for higher quality and efficiency. It can simplify our life by doing hard work for us. AI can do jobs smarter without feeling fatigued or having to take breaks like a human employee. It can control mechanisms that work in dangerous environments.
What is the difference between automation and AI, and why does it matter?
There is a difference between what we call automation and AI. When we automate things, we actually build algorithms with step-by-step instructions for getting from point A to point B—with a roadmap of every corner and how many steps it takes to get there. When it comes to AI, we tell it the end goal we need to achieve. We help it learn algorithms and how to find a decision. However, we do not reveal precisely the way to achieve it. This can make it harder to understand how AI makes decisions.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/07/22/is-it-time-to-agree-on-an-ai-bill-of-ethics/?sh=7284c8203eac

The world has been wowed by the newest displays of text-to-image technology by DALL-E 2 from OpenAI and Imagen from Google. Beautiful, amazingly creative compilations all generated by artificial intelligence (AI) systems. This is possible because AI has learned natural language understanding by looking at countless texts and images. 
Today’s systems have been trained to output new images when text is entered alongside pictures, uniting two seemingly disparate things in unique ways, much to the delight of viewers. A traditional image, such as that of an oil painting, can be co-opted to express something new or evoke a completely different feeling. It’s a new way to create.
Transforming creativity 
Imagine that with this AI technology, users no longer have to scroll through tons of image results to find the best content for their needs. In contrast with image search, people create something totally new, something that has never existed, something that perfectly suits their desires, whims or content direction. All they have to do is type in what they want, and the AI will draw images and construct photos as described in the given text, i.e., “Please give me a photo of a restaurant with a VentureBeat sign on the window that is on Mars.” New systems will return such photos. In essence, the system is an AI designer.
Such creative power grows exponentially when also deployed to make videos from text that describes a situation and mood, and which incorporates virtual actors. Or when text + AI construct the music to go with videos and images.
Text-to-video or music technologies are already commercially available and continue to be refined, opening up the potential to add more creative processes. If AI can draw images or design, human designers’ roles could evolve. Having brilliant and inspiring ideas would become more important, and the ability to discriminate the best output from the worst will be crucial as drawing skills might be replaced by AI. Those less skilled could also develop their own creative products with lower effort. 
Continue reading: https://venturebeat.com/2022/07/23/the-future-of-creativity-brought-to-you-by-artificial-intelligence/

There are many grand promises about the power of artificial intelligence. When we talk about the future of technology, AI has become so ubiquitous that many people don’t even know what artificial intelligence is any more. That’s particularly concerning given how advanced the technology has become and who controls it.
While some might think of AI in terms of thinking robots or something in a science-fiction novel, the fact is that advanced AI already influences a great deal of our lives. From smart assistants to grammar extensions that live in our Web browsers, AI code is already embedded into the fabric of the Internet. 
While we might benefit from the fruits of advanced AI in our daily lives, the tech companies that have created and continue to refine the technology have remained mostly reticent about the true power of their creations (and how they have built them). As a result, we don’t know how much of our Internet life is steered by AI and the possible bias we unwittingly experience daily.
We recently got a rare peek behind the curtain into the AI dynamics driving one of the world’s most influential technology companies. Last month, an AI engineer went public with explosive claims that one Google AI had achieved sentience.
What is sentience?
Philosophers, scientists and ethicists have debated the definition of sentience for centuries with little to show for it. A basic definition implies an awareness or ability to be “conscious of sense impressions.”
Continue reading: https://asiatimes.com/2022/07/we-must-not-be-kept-in-dark-about-ai/

In a digital world fueled by a steady influx of data, achieving organizational excellence depends on giving everyone immediate access to accurate, up-to-date information. Organizational-wide communication and collaboration are vital. Mission-critical decisions, in particular, depend on the timely sharing of lessons learned and insights from all departments.
With new technology based on artificial intelligence (AI) and machine learning (ML), it’s easier than ever to share data effectively and consistently. Significantly, technology now enables businesses to tap the knowledge stored in one person’s mind and translate it into actionable data that anyone can leverage whenever they need it. This technology takes organizational communication and collaboration to the next level — an invaluable advantage in the pursuit of excellence.
Driven by data
The trend toward decision intelligence brings together processes and technologies to support “continuous, connected, contextual decision automation and augmentation,” according to Gartner senior research director Pieter J. den Hamer. Decision intelligence encompasses data-driven decision-making and the growing use of AI and ML to accelerate and improve data analysis. With the advantage of more data that’s also more relevant and trustworthy, data analysts can produce stronger insights — resulting in faster and more confident decisions, greater efficiency and productivity, and a sound foundation for business strategies.  
Continue reading: https://venturebeat.com/2022/07/24/how-ai-and-decision-intelligence-are-changing-the-way-we-work/