Everyone is familiar with delivery services such as Door Dash and Uber Eats, which allow consumers to order deliveries of food by accessing an app on their smart phone. Now, a small Florida-based start-up company is hoping to offer the same service nation-wide, featuring delivery by drone.
Zing Drones is gearing up to train potential Part 107 pilots in the use of the company’s winch technology developed for use on DJI Inspire 2 drones. At the outset, Zing will employ its own pilots to fly company-owned drones, with the ultimate goal of enlisting private drone pilots who will act as independent contractors, using their own drones to make deliveries, Ian Annase, Zing Drones founder and CEO, said.
Annase said the company plans sometime next year to hire a regional flight manager to begin the training process. As the company expands its operations into different U.S. cities, additional flight managers would be hired.
“We want the pilots to go through some training with Zing before they start flying with us to make sure they know how to operate the winch,” he said. “The pilots, if they have an Inspire 2 — that’s our go-to drone for home delivery — we would be able to attach our piece of hardware to it.”
Once trained, these pilots would then fly on fixed schedules, determined by the flight manager in their respective areas.
Continue reading: https://dronelife.com/2021/12/26/drone-delivery-start-up-zing-taking-the-uber-model-to-the-sky/

In the second edition of DQDeepTech, panelists Vivekdeep Gupta, Kaavya Prasad and Prashanth Swaminathan discussed application of blockchain in governance projects, enterprise, and medium- and long- term changes to be witnessed due to it. 
Vivekdeep Gupta, Country Manager, R3, talked about how blockchain is used for supply chain needs. One big use case of enterprise blockchain identified and being used at scale has been around supply chain finance, logistics, and trade finance. Blockchain solves the information asymmetry that exists among various parties. It also solves the archaic processes, especially in trade finance. There are multiple global and local cases where blockchain has added value. 
R3 runs two global networks. One is called Contour, focused on global letters of credit. The other is Marco Polo, focused on open accounts space. Domestically, there are a number of banks focused on domestic trade finance. Cycle time for processing a letter of credit in India was about 5-8 weeks. We have reduced that by 75% using blockchain-enabled technologies. Blockchain has found its sweet spot. It allows transparency and visibility for multiple parties over a single trade finance app. It also makes the lives of big corporates and SMEs much easier.
Prashanth Swaminathan, Partner and Head of Institutional Business, Woodstock Fund, said there are trends in public domain. One is convergence, where the underlying tech is consolidating. We foresee space for public blockchains to be here. With the bedrock of blockchain, there will be emerging technologies on top of it. This could be AI/ML, VR, etc. We are identifying layers of blockchain and middleware that facilitate creation of futuristic apps. Woodstock is an emerging technology investment firm, investing in early and growth stage distributed ledger technology (DLT) startups and companies.
Second theme is financialization. It is an extension of the DeFi langugge. In DeFi, over last 12-18 months, we have seen that proliferate and create lot of efficiencies. We will see lot more merging of DeFi with traditional finance. We will see supply coming into DeFi, at a global level. It will merge with demand from rea-life use cases. We will have access to supply of such liquidity. We are looking to build on these mergers, and solving real-world use cases.
Continue reading: https://www.dqindia.com/developing-and-managing-blockchain-innovation/

This has been suspected since the company chose to change its name from Facebook to Meta a couple of months back but this has been spelled out only one by its future CTO Andrew Bosworth in a note sent to its employees.
Long Overdue Entry
The company has long been trying to enter into the crypto and blockchain market in a big way, but its progress has been slow due to the various regulatory challenges it faces worldwide. Some critics feel that it has simply grown too big for the regulators to view it kindly any longer as the company looks to pivot its business more into the world of fintech and blockchain. The regulators are worried that the company might be stepping on their toes as it looks to wean away from the users from the real world to the metaverse by its rebranding and by delving more into virtual reality and crypto.
Bosworth signaled that the company would be interested to work with other Web3 companies but also sounded a note of caution as it has to ensure that it continues to operate within the regulatory framework in the coming months as it looks to pull off one of its most audacious moves in recent years.
Continue Reading: https://financefeeds.com/meta-looking-focus-blockchain/

#Web3 #blockchain #crypto Yahoo Finance's Dan Howley explains what exactly web3 is, how companies and brands are planning to use that online space, and the criticisms tech leaders such as Jack Dorsey are lobbing at venture capitalist firms investing into the platform.

With significant growth projected in the global IoT market over the next 6 years, the need to subsequently secure devices at the edge from attacks, safe and secure through the manufacturing process, and managed securely throughout the life of the product will follow a similar trajectory, predict experts at Sequitur Labs.
Recent reports indicate that the global IoT market is expected to reach nearly $1.5 trillion by 2027 as driving factors of increased demand of Smart Sensors, the development of Smart Cities and industry developments in the field of AI continue to proliferate, with a Compound Annual Growth Rate of 24.9 percent during the forecast period.
This market growth will propel a rise in the IoT security market by a similar percentage as organizations remain increasingly vulnerable to cyberattacks as devices come online. By integrating security solutions with IoT devices, device manufacturers will be able to provide customers with real-time protection from threats.
The importance of secure IoT devices
While connected devices deliver a plethora of benefits to businesses, the necessity of being connected to public networks and the internet leave them particularly vulnerable to attack. Look for increased attacks on IoT-enabled environments and infrastructure as the ability of cybercriminals to infiltrate connected systems continues to be a profitable endeavor.
IoT devices built on legacy technology without proper security protocols in place remain soft targets ripe for exploitation. Until device manufacturers implement security suites to handle threats to connected devices, expect to see even more high-profile cases of hacking and attacks to both private businesses and public institutions in 2022.
Continue reading: https://www.helpnetsecurity.com/2021/12/27/secure-iot-devices/

Particularly exciting is how advanced automation around data crunching unlocks customer personas and recommendation engines with intelligence from customer behavior regardless of the channel. You get better customer experiences and buyer journeys that ease the pains and inefficiencies around purchasing your products.
Businesses expect AI to impact their businesses. Esteemed industry analysts Gartner reports that AI is growing exponentially, with spending on Artificial Intelligence in eCommerce set to reach $7.3 billion per year by 2022- that’s up by over 20%. That’s a lot of investment.
What are AI-driven customer personas looking to solve?
Unifying your customer data for analysis lets, you understand your customers in a more meaningful way. You have real-time insights on valuable customer segments like your biggest spenders and most loyal.
When you lack the understanding you get from customer-centric insights, you can’t tailor customers to find the products they want because you lack the understanding you get from customer-centric insights.
Without personalization, customers buy products that don’t fit their needs as closely as they should and go for the ‘next best thing.’ Or worse for the business, customers end up not buying at all. Retailers leave money on the table by not showing relevant products – even when they have them. So none knows to buy the product.
Technology like marketing automation and customer data platform (CDP) fix problems that impede delivering a seamless buyer experience. This article shares three impactful ways to use AI and unified customer profiles to tailor experiences in real-time.
1. Continuously optimize Search 2. Customer personas from data 3. Real-time Recommendations
Search Optimization
An eCommerce store needs a site search that uses AI and natural language processing (NLP) to constantly learn from user behavior — because a users’ behavior constantly changes. That is, people and influencers like using trendy terms. These can trick some systems. For example, “fly” could refer to retraining a tent down or mean zippers. . The business world is changing in ways that have surprised models, yet technology will still give you the best gains as things become more normalized. Reading this means you will not be left behind when it comes to marketing transformation. You don’t need to become a fully AI-enhanced company overnight, but adopting key technologies can turbocharge your initiative. Computers can quickly crunch data and develop new personalized products for every customer with machine learning.
Consumers generate a lot of data. Customers engage with media brands across mobile, desktop, and connected devices throughout their day, with average screen times for some customer segments up to 12 hours per day. The advanced data processing with AI leverages the collected information from all of the shopping trips and analyzes to understand which behaviors mean a specific kind of purchase.
Continue reading: https://customerthink.com/how-ai-driven-customer-personas-can-transform-marketing/

For many companies, digitization and automation are the keys to the further development of additive manufacturing. Thus, more and more manufacturers are relying on cloud-based solutions and integrating various algorithms into their 3D printing solutions in order to exploit the full potential of the technology. As a digital process itself, 3D printing is part of Industry 4.0 and thus an important component of an era in which artificial intelligence, such as machine learning, is increasingly being used to optimize the value chain. Artificial intelligence (AI) is able to process a large amount of complex data in a very short time, which is why it is becoming increasingly important as a decision maker. We explain what machine learning is and why this form of AI is helping to shape the future of additive manufacturing.
Machine Learning is a subcategory of AI and is defined as a system or software that uses algorithms to examine data and subsequently recognize patterns or determine solutions. Contrary to a widespread belief that machine learning is a newfangled phenomenon, it can be said that its beginnings date back to the 1940s, when the first researchers started to recreate the neurons of the brain with electrical circuits. In 1957, the Mark I Perceptron was the first major success in this field: the machine was able to classify input data independently. In doing so, the device learned from mistakes made in previous attempts, which improved the classification over time. Since then, the foundation was laid and researchers became fascinated by the possibilities and potential of the technology. In the meantime, we encounter artificial intelligence every day in all areas of life. From speech recognition to intelligent chatbots to personalized treatment plans, Machine Learning is being used in a variety of applications.
Supervised vs. Unsupervised Machine Learning
Within the machine learning spectrum, it is important to distinguish between different methods and models. Not all machine learning is the same. For example, a distinction must be made between supervised and unsupervised machine learning. Supervised Machine Learning requires that categorized data (input data) and the target variable (output data) are available. From these, the model is derived, which then examines (new) uncategorized data and determines the target variable for these itself. This form of machine learning is used, for example, for predictions, e.g.: for forecasting maintenance intervals.
Continue reading: https://www.3dnatives.com/en/machine-learning-artificial-intelligence-additive-manufacturing-271220214/#!

From content recommendations on your Netflix dashboard to interactions with Amazon voice assistants to AirBnB, Uber, and Google—all couldn’t do what they are doing without AI.
But these are some of the world’s most successful companies. What about the rest?
This might be the intelligence era, but the vast majority of companies have yet to tap into its potential. And it’s not that they’re doing anything wrong. Big tech companies were data-first from the start. Smaller-scale companies with more traditional roots just aren’t built with the capability to harness AI in their day-to-day operations. And until very recently, such a capability remained far out of reach.
DECISION INTELLIGENCE AND THE NEW BUSINESS REALITY
What’s changing the intelligence game for businesses is a new AI category that’s built for commercial settings: Decision Intelligence (DI).
This exciting technology is helping companies in sectors outside of tech to layer in AI-informed decision-making through every vertical of the business—from supply chain to marketing. DI is set to help a much broader spectrum of businesses harness data to make better decisions. Gartner predicts that over a third of large organizations will be using it within the next two years.
It makes sense that the commercial application of AI should be focused on decision-making. The value of a business is the sum of its decisions: A product positioning or logistics approach that cuts ahead of the competition, grows revenue, and funnels back into the value chain.
We can look at DI as the leap from hoping we’re making a decision that will create value for a business—to knowing we are. In the computing age, we’d use historical data to make a guess at good forecasting, pricing, or marketing decisions. In the age of DI, real-time data becomes endemic to the decision-making process, so we can be confident in the outcome every time.
In this new business reality, data teams are no longer hidden away in a back office, building models that never see the light of day. They’re in constant communication with the commercial side of the business, absorbing data from every department, and translating it into immediately actionable recommendations.
Suddenly, we’re seeing workforces where every employee—from the process level to the C-suite—is empowered to use AI in their everyday decision-making.
THE PATH TO DI ADOPTION
This is what the very near future could look like. But what’s the path to adoption for companies who want to start embedding DI? I typically break this down into three key requirements:

• an AI-ready data set
• an intelligence customized to your specific business
• an interface available to teams company-wide so that non-technical teams can engage with a model and its outputs

For the majority of companies, though, building all of that is a tall order. That’s why I think we can expect a growing demand for off-the-shelf DI platforms in the next couple of years—a trajectory similar to what we’ve seen with CRMs. In the early 2000s, 80% of companies were building CRMs in-house. Today, we’d never dream of it. Companies are accelerating time to value by investing in ready-made solutions—and DI is ripe for the same kind of innovation.
Continue reading: https://www.fastcompany.com/90706205/this-new-kind-of-ai-is-built-to-help-you-make-better-decisions

The robust development of artificial intelligence (AI) technologies has made it possible to apply such developments in practices across a wide range of daily human activities. The progress of machine intelligence capacity has allowed business leaders to significantly increase the level of business process automation.
In 2019, Adobe reported that 15% of enterprises surveyed actively used AI technologies in their operations, while representatives of another 31% of companies started seriously considering introducing such innovations within 12 months of the survey. As time passed, and the range of AI-powered solutions continued to expand, more and more enterprises considered deploying AI in some form or another. In a report from Deloitte, 83% of adopters surveyed indicated that they consider AI-powered technology to be one of the top business priorities throughout the digitalization journey. Hence, AI-powered operating processes are the inevitable future of humankind, which will allow individuals to automate routine tasks and companies to increase operating efficiency and thus boost revenues and cut costs. The big question for today is: What are the future trends in AI application in our lives and business environment?
Breakthroughs And Challenges In AI Development
AI development and use is currently an issue to focus on. Specialists and mass media expect technology advancements to allow complete redefining of the role of machine intelligence in modern life. This is about the development of computer vision, natural language processing and neural networks. The adoption of AI-fueled solutions within big tech continues to gain momentum. One of the biggest examples dates back to 2013 when it was reported that 35% of all online purchases on Amazon were made based on the recommendation algorithm, which advises suitable, and similar goods for customers.
Social needs lead to automation of even the most old-fashioned and conservative parts of the economy, such as agriculture. AI has the ability to dramatically transform the way humans do farming. U.S. farmers are already conducting (subscription required) experiments using robotized pest control technologies, and Russian farms are testing unmanned harvesters for cereal crops. But the scope of AI abilities within farming doesn’t end here, as AgTech company Indigo is using AI and big data-powered genome sequencing techniques to predict the most beneficial microbes for plants in order to increase crop yields and reduce risks.
Continue reading: https://www.forbes.com/sites/forbesfinancecouncil/2021/12/27/how-machine-intelligence-is-changing-the-modern-world/?sh=8c1e2565936c

Introduction
This article discusses the global AI smarter camera market, explores different camera use cases, highlights the innovations in the logistics and transportation industry, and includes recent interview highlights with, Chris Piche, CEO of Smarter AI, a USA headquartered company that has a compelling product platform and ecosystem community vision beyond many of the incumbent players. In a nutshell, Chris Piche, CEO of Smarter AI, described his company as “the leader in AI cameras and enablement software. Smarter AI software-defined cameras program AI-like apps on a phone and are supported by AI Store, our ecosystem of AI models and developers, to scale AI camera use cases.” The Las Vegas company, with offices in Singapore and in Dubai, has also recently secured its Series A financing of over $30M to advance its scale-up enablement needs, and from all early signals, Smarter AI is heading in the right direction.
Market Dynamics
The global smarter camera market, according to BlueWeave Consulting, the market was worth USD $7.4 billion in 2020 and is further projected to reach USD $33.3 billion by the year 2027, growing at a CAGR of 24.0% in the forecast period.
What is very exciting about the smarter AI intelligent camera market is that it uses so many diverse technologies to create new capabilities, including: computer vision, image recognition, machine learning, deep learning, speech and voice recognition technologies. These methods are further augmented by data scientists and computer engineering specialists to build AI models that can also solve different business use cases – as the world continues to decompose business problems faster using these advanced methods.
These intelligent cameras solve many business challenges ranging from: driving assistance systems, accident reconstruction intelligence to security surveillance cameras for preventing crimes at retail stores, or providing increased security in residences. The AI smarter camera technology can capture everything around it: the landscape, the intensity of light, the decomposition of every section of the image for precise classifications, the timing of the footprint, the situational awareness, the sound and noise detection, even the gait of the person walking around a vehicle can provide health intelligence – the possibilities for breakthrough innovations are simply endless.
Smarter AI-enabled cameras have been for some time enabling law enforcement agencies to identify any unwanted activity and prevent crimes. At the same time, there are growing concerns from many public channels on the risks of society creating a surveillance driven world, where humans no longer have the right to be invisible, unseen or even unknown. All these dynamics are putting increased strain on policy, ethics and governance regulators, as well as challenging legal legislators to create a more balanced technology and human interface world and set new laws more rapidly as once again technology innovators are out front of legal legislators.
Continue reading: https://www.forbes.com/sites/cindygordon/2021/12/26/ai-camera-intelligence--a-company-to-watch-smarterai/?sh=3f6ebd3c206e

The ancient stoic philosopher Epictetus once said, “It's not what happens to you, but how you react to it that matters.” Though these words were spoken nearly 2,000 years ago, they are still rooted in so much truth today. During recent years, society has navigated the Covid-19 pandemic, racial injustice, supply chain havoc, and so many more unseen challenges. Yet innovation and progress have continued to unfold.
With 2022 on the horizon, it’s important to take a critical look back at the past months in order to take inventory on how it has affected us all. For the following 33 trailblazing women, they’ve not only reflected on 2021 and the challenges it’s brought, but they’ve chosen to apply all they’ve overcome to ultimately propel them into the future. 
For many, life has been a seemingly endless uphill battle. However, the new year is a welcome symbol of hope for all that’s to come. 
Jill Goldenziel
 
Professor, speaker, and consultant at Jill Goldenziel, where she speaks and consults on law, leadership, and international affairs.
Public speaking opportunities declined during the pandemic. I used that time to invest in my geopolitical risk consulting business, to network in the field, and to work on my book about how politicization of refugee and migration crises harms national security. As a result, my geopolitical risk consulting business doubled in 2021 and I expect this growth to continue in 2022. And when the book comes out, the tour will bring me all of the speaking engagements and travel that I've been missing!
Nathalie Molina Niño
 
Managing director at Known Holdings, a financial services growth platform for the new majority and the multi-trillion dollar economy we power. 
Many companies I've invested in struggled to stay afloat in 2021. As we go into the new year, I'm more committed than ever to the idea of investing as a way to create justice in systems that are deeply unjust. As a result, I'm thrilled to be able to finally tell the world about the company my partners and I have been building since 2020, Known Holdings.
Continue reading: https://www.nasdaq.com/articles/women-to-watch-on-how-the-challenges-of-this-year-are-propelling-them-forward

Here’s a “real-world” guide on what to expect if you decide to start your own drone business in 2022. Pros and cons.
Back in 2015, I decided to start flying drones commercially. The DJI Inspire 1 had recently been released, and it was clear that drones were going to open up a lot of video production possibilities. I had just went full-time as a freelancer, primarily doing local commercial work, making tutorial content, and selling stock footage.
Drones looked like a perfect addition to my current “toolkit” of video offerings. I also liked the idea of diversifying the type of video work I could do. (Plus, drones would give me more opportunities to work outside.)
And so, Ozark Drones was born.
Over the years, I’ve surprisingly had several opportunities to work with name-brand companies and television shows, even though I’m located in rural Arkansas. So, don’t be discouraged if you aren’t located in a big city.
This article will serve as a “real-world” perspective on what you can expect if you decide to start your own drone business or side hustle. I won’t sugarcoat the process, and most likely, drones won’t magically become your primary income. (But, they’re a lot of fun!)
And, obviously, starting a drone business now will be much easier than it was back in 2015.
FAA Requirements and Cost
Flying a drone “commercially” essentially means that you can legally operate a drone for business purposes and get paid for the service. In order to fly drones commercially in the U.S., you’ll need a Remote Pilot Certificate from the FAA. (This is also often referred to as a Commercial Drone License or Part 107 License.)
In short, to obtain this certificate, you must pass a Knowledge Test at an FAA-approved Knowledge Testing Center. Currently, the cost is $175.00.
Don’t let the Knowledge Test intimidate you, though. The FAA provides a full outline of the steps required to become a drone pilot. The FAA also provides a free study guide for the Knowledge Test.
Having a Remote Pilot Certificate is key when it comes to getting bigger clients for drone work. (Plus, it’s legally required for all commercial work!)
Continue reading: https://www.shutterstock.com/blog/drone-business-essentials

New restrictions on a key dronemaker show how serious the U.S. is about cutting its reliance on Chinese technology, said Bruce Einhorn and Todd Shields in Bloomberg. China's DJI Technology is "the world's top producer of unmanned aerial vehicles" and controls "more than half of the U.S. drone market." But the Treasury Department last week added DJI and seven other Chinese tech companies to a growing "blacklist," blocking it from receiving any U.S. investments. Though DJI is a private company, it "has become the poster child for a much wider national security threat" — China's "ability to obtain sensitive data on millions of Americans," as "everything from cars to yoga mats to toilets are now transmitting data." Harnessing that information is viewed as a "key to dominating technologies like artificial Intelligence" — and "exploiting weaknesses in strategic foes." The move against DJI echoes how the U.S. started its campaign against Huawei, China's leading phonemaker, said Gina Chon in BreakingViews. But "it was relatively easy to make" the Chinese telecom disappear from the U.S., because it was just making its first inroads. DJI is a different story. "More than 900 U.S. public safety agencies use its products," including the New York Police Department, making a commercial ban "unrealistic."  The pressure to disengage, though, comes from both countries, said the Financial Times in an editorial. China pressured Didi to delist shortly after it "launched the biggest listing of a Chinese company since Alibaba in 2014," and has allowed a "slow unraveling" of property giant Evergrande, which defaulted on debts held by foreign investors. The moves seem to be part of "a bulwark" against "mistrusted foreign forces" as Beijing constructs a new "Fortress China."
ine — let's shut our doors to China, too, said Henry Olsen in The Washington Post, even if it hurts corporate profits in the short term. U.S. companies are now "self-censoring anything that might offend the Communist Party of China." It was reported recently that Apple secretly agreed to a $275 billion deal in 2016 to buy more Chinese-made components in return for reduced Chinese regulatory pressure. That and "hundreds of other corporate decisions" have helped China design weapons systems "more sophisticated than our own." Our addiction to China's cheap goods is "endangering our national security." China is not a stable market for U.S. corporations, anyway, said Desmond Lachlan in The Hill. President Xi Jinping's recent "clampdown on the high-tech sector" in the pursuit of "common prosperity" threatens China's future growth. Meanwhile, the country's property sector is imploding. "One has to wonder whether the Chinese economy might prove to have clay feet."
The U.S. still should fear China's technological gains, said Graham Allison and Eric Schmidt, the former CEO of Google, in The Wall Street Journal. Experts say it "could soon be the global leader" in artificial intelligence, semiconductors, 5G wireless, quantum computing, biotechnology, and green energy — if it doesn't already hold the dominant position. The U.S. holds sway in aeronautics, medicine, and nanotechnology, but China has "emerged as a serious competitor" in these fields, too. Lawmakers are just "beginning to wake up to this reality."
Continue reading: https://theweek.com/china/1008395/drone-wars-us-imposes-new-sanctions-on-china

We are in an era where AI is gradually introduced in all the sectors, be it businesses, gaming, gadgets, sports, farming, medical, or health tech. 2021 is around the corner and we are all set to enter into a new year. To cherries the last month of 2021 we had an interaction with founders, and CEOs of several companies from different sectors to understand how AI is empowering the world around us and what will be the future. 
In a conversation Gaurav Singh, Founder, and CEO, Verloop told us, AI or rather Conversational AI has been dynamically changing the way brands connect, interact and engage with their customers. It helps organizations meet customer demands – be available 24×7, respond quickly, personalize the interaction, and most importantly, provide options for customers to choose their channel and language of communication as per their convenience. There are four essential elements of Conversational AI, namely Machine Learning, Natural Language Understanding, Automatic Speech Recognition, and Text-to-Speech and Speech-to-Text. Using these elements, Conversational AI interacts with customers through chatbots and voice bots and enhances their experience. With the help of these contextual, natural conversations, customers can find relevant information, resolve queries, transact, track orders, and so on.
Another advantage of using Conversational AI is that it offers a consistent experience across channels. Most customers don’t like to hold or wait for long to connect with an executive to resolve their query. Conversational AI plugs the need gap in this regard by offering a fast response along with adding a personalized touch. For a lot of customers, personalization is an important consideration. With the help of AI-powered customer service, brands are able to maximize their customer satisfaction scores. It is a proven fact that satisfied customers are more likely to offer repeat sales. Not only is conversational AI a cost-effective tool for customer satisfaction and retention but also helps in maximizing sales. 
As these chatbots continue to evolve, conversational AI is also now moving towards adopting voice-based assistance. The market for voice assistants is growing rapidly as it helps consumers to move towards hands-free mode and allows them to multitask.
Gautam Virk -Co-Founder & Chief Operating Officer at NODWIN Gaming expressed his views saying, “AI for long has been in use to create highly complex, responsive and adaptive non-player characters that can match or surpass human intelligence. While that's the case for most RPGs and other story-based games, AI in esports is used in various other aspects.”
Continue reading: https://www.pinkvilla.com/tech/news/how-ai-is-empowering-the-world-around-us-and-what-ceos-have-to-say-about-it-975209

To predict the future, sometimes we must take a careful look at the past. Cyber crime rarely works in 12-month cycles—it’s more fluid than that. So we can often spot the first signs of impending trends some time in advance. In this regard, 2021 has been instructive. We’ve seen more advanced and targeted ransomware actors looking to use zero-day exploits to compromise their victims. We’ve also seen an increase in dark web chatter about the use of deepfake technology and other AI-powered fraud techniques to support business email compromise (BEC) and similar scams.
Both could become a regular feature of 2022. But forewarned is forearmed, especially when it comes to cyber security.
Ransomware ramps up
Ransomware was the stand-out story of 2021. But amongst the headlines, one of the most interesting trends we’ve noticed is the increasingly aggressive way some groups are going after targets. We all know about phishing vectors, collaboration with Emotet and TrickBot groups, and exploitation of RDP and VPN infrastructure. But what about supply chain attacks using multiple zero-days?
That’s exactly what happened in a sophisticated campaign linked to the Clop group or its affiliates. It involved compromise of the legacy FTA file transfer service from Accellion, which impacted dozens of downstream customers, from global law firms to aircraft manufacturers. This was a highly targeted, well-planned operation from start to finish, which didn’t even use ransomware at all — relying solely on data exfiltration for extortion.
Of course, researching and exploiting four zero-day vulnerabilities doesn’t come cheap. But some ransomware actors now have hundreds of millions in stolen funds to their name, and the market for such exploits is growing. Expect more of the same in 2022.
Continue reading: https://www.information-age.com/how-ai-powered-fraud-and-aggressive-ransomware-could-dominate-2022-123498340/

Amnon Mishor is CTO and Founder. Lead space, Recognized in the industryAn AI-powered buyer data platform used by B2B companies such as Zoom and Salesforce.
In the past About 10 years, the digital revolution has given us surplus Of the data. This is exciting for many reasons, but mainly in terms of how AI can revolutionize businesses further.
But in the B2B world, where I’m deeply involved, there’s still a shortage of data, mainly because of the significantly lower number of transactions compared to B2C. that’s why, AI fulfills its promise to revolutionize businesses, We also need to be able to solve these small data problems. Thankfully, you can.
The problem is that many data scientists turn to bad practices and generate self-fulfilling prophecies that reduce the effectiveness of AI in small data scenarios and ultimately AI’s influence on corporate development. Is to prevent.
The term “self-fulfilling prophecy” is used in psychology, investment, etc., but in the world of data science it can easily be described as “clear prediction”. This is seen when a company predicts what is already working “by design” and finds a model that applies it to different scenarios.
For example, retailers determine that people who fill their carts online are more likely to buy than those who don’t, so they sell in large quantities to that group. They are predicting the obvious!
Continue reading: https://californianewstimes.com/how-to-apply-ai-to-small-data-problems-techcrunch/634675/

With small and big businesses racing ahead to incorporate new and better technologies into their production models, artificial intelligence (AI) use is inevitable. Whether it is automation, efficient production or forecasting production, or consumption patterns many businesses are already AI dependent. That being said, it is equally necessary to strike a balance when it comes to AI for business. The key lies in avoiding passive uses of AI while simultaneously preventing an overhaul all at once. AI software companies suggest scaling AI for making businesses better and faster. But is that enough?
The scope of AI is limitless and it does have the potential to make a substantial difference within minimal time. No, AI is not overhyped. Yes, your organization can do better with it. Scaling AI comes with its own set of difficulties; insufficient data infrastructure and the absence of an AI governance model are significant barriers. However, it is still worth the time. Successful organisations have not just incorporated AI systems into their production model. They have simultaneously made attempts to go beyond their early AI efforts and improvise them. The essence of flourishing businesses lies in their persistence: persisting through the initial arduous phases of scaling AI and reaping benefits later. Whether it is achieving growth objectives faster, managing costs, or extracting maximum value out of your data, scaling AI is the answer. Below is a guide to best practices for scaling AI that are nourishing for your organisation. 
It’s All About Governance
The difference between a successful organisation and an unsuccessful one lies in the quality of management of Data and AI governance models. These are important parameters of scaling AI. Organisations that have strong Data and AI governance management systems thrive better. This has to do with mapping interrelated processes, creating integrative solutions, and identifying the leadership and talents needs. 
When Tactic Meets Strategy
Getting the right AI strategy for your business can be tricky. This is why organisations must identify which business areas will experience greater growth in a lesser, more reasonable time. Does the bottom line of the organisation grow? Are customer experiences improved? These questions are prime concerns and must be asked. In this scenario, organisations must weave tactical and strategic objectives together and map the interconnections within their business models. Going beyond proof of concept and incorporating data and AI into the wider digital functioning of the organisation, in the long run, is a must to achieve a competitive edge. 
Continue reading: https://www.analyticsinsight.net/how-scaling-ai-can-benefit-your-organisation-a-guide/

Machine learning is the process of using computers to detect patterns in massive datasets and then make predictions based on what the computer learns from those patterns. This makes machine learning a specific and narrow type of artificial intelligence. Full artificial intelligence involves machines that can perform abilities we associate with the minds of human beings and intelligent animals, such as perceiving, learning, and problem-solving.
All machine learning is based on algorithms. In general, algorithms are sets of specific instructions that a computer uses to solve problems. In machine learning, algorithms are rules for how to analyze data using statistics. Machine learning systems use these rules to identify relationships between data inputs and desired outputs–usually predictions. To get started, scientists give machine learning systems a set of training data. The systems apply their algorithms to this data to train themselves how to analyze similar inputs they receive in the future.
One area where machine learning shows huge promise is detecting cancer in computer tomography (CT) imaging. First, researchers assemble as many CT images as possible to use as training data. Some of these images show tissue with cancerous cells, and some show healthy tissues. Researchers also assemble information on what to look for in an image to identify cancer. For example, this might include what the boundaries of cancerous tumors look like. Next, they create rules on the relationship between data in the images and what doctors know about identifying cancer. Then they give these rules and the training data to the machine learning system. The system uses the rules and the training data to teach itself how to recognize cancerous tissue. Finally, the system gets a new patient’s CT images. Using what it has learned, the system decides which images show signs of cancer, faster than any human could. Doctors could use the system’s predictions to aid in the decision about whether a patient has cancer and how to treat it.
Continue reading: https://scitechdaily.com/science-made-simple-what-is-machine-learning/

Web3, the next-generation internet that includes the metaverse, is far more hype than reality. But a battle is brewing over whether an internet based on blockchain technology will be decentralized—as crypto advocates would like—or controlled by Big Tech.
Jack Dorsey, co-founder of Twitter and Square, has weighed in with tweets, taking a shot at companies aiming to control—or try to dominate—Web3.
“You don’t own web3. The VCs and their LPs do,” Dorsey tweeted Tuesday, referring to venture capital funds and their limited partners. “It will never escape their incentives. It’s ultimately a centralized entity with a different label.”
Tesla CEO Elon Musk piped up, tweeting “has anyone seen web3? I can’t find it.” MicroStrategy (ticker: MSTR) CEO Michael Saylor, a Bitcoin billionaire, chimed in, too. #Web3 is marketing,” he tweeted.
As tech visionaries see it, decentralized blockchain technology will be at the core of the next-generation internet. The idea is that everything from payments to social networks to e-commerce will be distributed over a global computing network that is run and governed by its users—not large companies.
Cryptocurrencies like Bitcoin or stablecoins, designed to maintain a fixed value, would play a central role, displacing fiat currencies like the dollar. Ideally, according to the crypto libertarians, no corporate entities would maintain control, and transaction fees would be distributed to the network’s operators, similar to how Ethereum and other blockchains now work.
Dorsey, of course, is a big crypto advocate. He changed the name of Square to Block (SQ), reflecting the payment company’s burgeoning shift to a crypto services and blockchain business. It’s in Dorsey’s financial interests to promote Bitcoin and blockchain technology, as it is in the interests of Musk and Saylor.
And Dorsey has benefited from venture capital: Square raised $601 million, including one funding round in May, according to Crunchbase. And venture capital has funded Tesla and many companies now aiming to profit off blockchain technology and cryptos.
Web3 and the metaverse won’t be coming tomorrow or in the near-future, though, since the technology is still far behind the hype. Even if the crypto-libertarian ethos prevails, legions of companies are likely to capitalize, just as they are by bringing blockchain technology and cryptocurrencies under corporate ownership now.
Continue reading: https://www.barrons.com/articles/metaverse-crypto-elon-musk-jack-dorsey-blockchain-big-tech-51640199480?tesla=y

Meta, Facebook’s parent company, is aiming for “deep compatibility” with blockchain technology, according to an internal post on Tuesday from a top executive.
In the note to employees, which was obtained by The New York Times, Andrew Bosworth, who will become Meta’s chief technology officer next year, laid out a vision for the social network to adopt and work with various blockchain or cryptocurrency technologies that have collectively become known as web3.
Mr. Bosworth urged caution but said the company should look to adopt the technologies before others, noting that blockchain technology — which are essentially distributed ledger systems — could have “profound impacts on our industry over the next decade.”
“My overall guidance is to target a deep compatibility with the blockchain,” he wrote. “There aren’t many places where I expect us to depend on it exclusively yet, but if we see an opportunity to work jointly with entrepreneurs in the web3 space I expect it will be worth the effort.”
Technologists, entrepreneurs and investors in the tech industry have debated the internet’s future architecture, with some believing that the decentralization offered by blockchain technology is a way to wrest power away from giants including Meta and Google.
But while Google has been reluctant to dive into crypto, Meta has experimented with cryptocurrencies, including an effort to create a global digital currency that could be used by Facebook and WhatsApp users. The head of that crypto project, David Marcus, announced his departure from Meta last month after the digital currency was rebranded and faced scrutiny from regulators.
Continue reading: https://www.nytimes.com/2021/12/22/technology/meta-facebook-web3-blockchain.html

What’s next for drone regulation in 2022?  A new report from Drone Industry Insights says the commercial industry can expect progress globally.
DRONEII Editor Ed Alvarado writes that around the world, drone regulations – and the regulatory framework – are evolving rapidly.  “This is a very welcome development given that the drone industry sees this as the most important driving factor.
The movement on drone regulation in 2022 is global.  In Korea, significant movement towards urban air mobility is underway: continuing the progress made this year with trial flights and the government commitment to an early implementation of passenger VTOL aircraft.  In the United States, the FAA is on the cusp of defining rules for flight Beyond Visual Line of Sight (BVLOS) after establishing the BVLOS ARC this year.  In Brazil, the government is simplifying drone registration: in China, officials are publishing a new, risk-based regulation framework.  Japan will finalize registration and BVLOS flight standards.
These developments signal progress towards global drone integration and an expansion of commercial drone operations.  And, writes Alvarado, taken together these global developments signal that the drone industry is getting closer to reaching its potential and scale.
Continue reading: https://dronelife.com/2021/12/22/drone-regulation-2022-drone-industry-insights-on-what-comes-next/

In recent years, many leading companies have fallen victim to high-profile cyberattacks and data breaches. Cybercriminals and hackers around the world develop new methods and techniques to break into and compromise even the most advanced security systems and gain access to sensitive information. This has led to the exposure of not only trade secrets but also the private information of millions of users.
This requires that all organizations stay vigilant and protect themselves. However, not all have the required tools, staff, time and, most importantly, talent and skills needed to overcome these challenges. This has, unfortunately, led some to succumb to cyberattacks. That’s where an artificial intelligence (AI) virtual assistant can come into play.
How can AI virtual assistants help security analysts?
A digital cybersecurity analyst is a type of virtual assistant — in other words, a smart system built using artificial intelligence. It’s capable of learning, in real time, from the experiences of security analysts and by observing an organization’s data. Its ability to process large amounts of data with speed can help security analysts better defend their organizations against cyberattacks. In addition, the insights that a digital cybersecurity analyst offers can free up time for security analysts to perform more valuable tasks, such as exception handling and enhanced risk suppression.
Many organizations, regardless of their size, type and industry, can utilize digital cybersecurity analysts to save both time and resources. These domain-specific AI virtual assistants can automate certain processes and enforce countermeasures to increase the productivity, effectiveness and efficiency of security teams. This can allow organizations, especially ones with small security teams, to determine the right course of action to counter attacks.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2021/12/22/can-ai-virtual-assistants-help-close-the-gaps-in-cybersecurity/?sh=39a6c961fd64

Evolution Of Marketing
Technology has transformed the way brands communicate and engage with potential customers. About 100 years ago, people relied on paper flyers, newspaper ads, billboards, direct mail and in-person interactions as a form of marketing to promote their business. In today’s digital-first landscape, marketing has become more reliant on the internet, social media and online advertising.
Marketing has evolved over the past decades, and we’re now in an era of digital revolution where marketing is completely dependent on consumers who dictate what content they want to receive. The growth of AI technology has opened up even more opportunities for marketing professionals who can benefit from AI automation. AI-powered marketing saves businesses a lot of time and resources spent on gathering and analyzing data and helps fully optimize the performance of marketing campaigns, which would otherwise be impossible in the case of traditional marketing campaigns.
Here I will go over some great ways artificial intelligence can benefit your marketing.
Predictive Marketing
Marketers have long used data to understand consumer behavior and trends, predict future needs and optimize their campaigns accordingly. Data-driven marketing planning has become even more advanced with the advent of AI technology. Using historical data models and statistics with a combination of AI solutions, predictive analytics delivers advanced insights to understand campaign effectiveness, predict future behaviors and make better marketing decisions.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2021/12/22/how-to-use-the-power-of-ai-for-marketing-success/?sh=e182452783e0

1) Language AI will take center stage, with more startups getting funded in NLP than in any other category of AI.
Language is humanity’s most important invention. More than any other attribute, it is the defining hallmark of our species’ intelligence.
Naturally, language pervades every facet of every business activity across every sector. The ability to accurately automate language therefore opens up virtually unbounded opportunities for value creation.
The field of natural language processing (NLP) has been upended and turbocharged in the past few years by a foundational new technology known as transformers, first introduced by Google researchers in a 2017 paper. We are only now reaching the point at which this dazzingly powerful technology is mature enough to be productized and commercialized at scale. A revolution in language AI, and thus in business, is around the corner.
Venture capitalists will plow record amounts of money into NLP startups in 2022. Leading NLP startups Hugging Face (last valued at $440M) and Cohere (last valued at $200M) will both become unicorns next year.
In the months and years ahead, expect a Cambrian explosion in NLP startup innovation as entrepreneurs identify a vast array of language-based activities across the economy to optimize, automate and transform using AI.
2) Databricks, DataRobot and Scale AI will all go public.
These three companies are among the first wave of big winners in the modern AI economy. They each provide tools and infrastructure to help other companies build AI, reflecting the common theme across technology cycles that infrastructure precedes applications.
All three companies boast astonishingly high revenue growth rates. All three raised big rounds in 2021 from “pre-IPO” investment firms, which typically invest in companies shortly before they go public: Databricks from Franklin Templeton; DataRobot from Altimeter and Tiger Global; Scale AI from Dragoneer, Greenoaks and Tiger Global.
Companies often make a high-profile CFO hire in preparation for an upcoming IPO. DataRobot announced this April that it had hired Damon Fletcher (formerly Tableau CFO) for the role. Databricks CFO Dave Conte, meanwhile, previously served as CFO of Splunk, where he took the company public in 2012. Don’t be surprised to see Scale AI make a high-profile CFO hire early in the new year.
Continue reading: https://www.forbes.com/sites/robtoews/2021/12/22/10-ai-predictions-for-2022/?sh=1c956546482d

According to Research and Markets, the global Single Sign-on Market is predicted to reach $2.2 Billion by 2027. SSO deployment is a complex and time-consuming task for legacy solutions. Knowing possible difficulties with the existing approaches, MobiDev experts went further to coin their own approach. 
Our experience is related to the SSO implementation between two mobile applications built with different versions of NativeScript – app1 and app2. But in fact, the described approach to SSO is identical for both native and cross-platform applications. 
Using SSO between mobile applications requires opening the browser, WebView, InAppBrowser, which will log in and, after closing, open a second application. We strived to create something more convenient and found the solution that suited everyone.
The solution lies in opening one application from another via the deep link functionality (deep links send the user directly to the specific in-app location) – and then the magic happens.
Each application will have its own settings and IDs that are unique in the system of the identity server. All changes will be made only by UI teams, and the solution will work with any identity providers that have code flow.
Continue reading: https://www.iotforall.com/press-releases/single-sign-on-implementation-for-enterprise-applications