Enterprise deployment of artificial intelligence (AI) is positioned for tremendous growth.
Artificial intelligence is set to change the business world by improving predictive analytics, sales forecasting, customer needs, process automation and security systems.
IBM’s Global AI Adoption Index revealed that a third of those surveyed will be investing in AI skills and solutions over the next 12 months.
More expansive use of AI democratizes AI, providing access to insights to more people – technologists and non-technologists alike. The latter group might include people in leadership, sales, finance, human resources and operations. This is where AI will shine, empowering business teams to make AI-driven decisions.
Imagine: business teams do not have to know how to code or be schooled in the intricacies of AI’s backend. Instead, they will use AI like you and I use a mobile phone for efficiency (if we’re running late, we merely send a text notifying the other person), access information faster (if we’re in the grocery store and need a recipe, we look it up), make better decisions (GPS gives us the fastest route).
Just as mobile technology works without us understanding complex circuitry, algorithms or software, the democratization of AI across enterprises will be integrated in much the same way.
So, what will hold AI back and how will AI help enterprise companies gain traction?
3 Obstacles and Opportunities Organizations Face by Implementing Artificial Intelligence
Artificial intelligence deployment approach | Source: IBM
Obstacle #1: Data in disarray. Data that does not provide a complete picture and single version of truth because of data silos and various data formats within an organization.
Opportunity: Employing a data fabric. Using a data fabric to help organizations use data more effectively and get the right data to users regardless of where it is stored. One significant advantage of a data fabric is that data governance rules may be automatically set for compliance.
Having one information structure to garner insights and analytics from, integrating security to protect sensitive data and establishing a framework for implementing trustworthy AI positions AI as part of the business strategy, not solely an IT strategy so that AI directly impacts business operations.
It all comes back to connecting data with business drivers and a data fabric helps accomplish this. It is what I call “point-to-point” thinking – knowing the business imperatives, business drivers, the different levels of raw data, who is consuming the data, who will have access to the data, and why the data is important in decision-making and then, the big payoff with AI, how it will elevate experiences: customer experience, workforce experience, supply chain experience, strategic partner experience, community experience. In “point-to-point” thinking we don’t hoard data, but share it – securely.
Obstacle #2: Varied skill levels. A lack of AI technical skills across the enterprise and a reliable, open platform to bring AI to more people.
Opportunity: Creating a bridge to AI for people within the enterprise. Palantir for IBM Cloud Pak for Data is one of the great innovations of our time because it doesn’t require coding skills. People in non-technical roles can go from raw data to data insights quickly using application templates (think of all the designs being produced with minimal design experience because of apps like Adobe Photoshop and Canva). This is truly the path to democratizing AI.
People can now use AI to make better decisions in real time and improve business outcomes. These teams include sales and marketing, manufacturing operations, campaign managers, branch managers, franchise operators, human resources, among others.
An example: a customer walks into their regional bank. The banking professional greets the customer, invites them to sit down and pulls up their profile. They see, not only account information, but a 360-degree view of the person sitting across from them. Through a data fabric, non-tabular visualizations gathered from previously siloed data originating from different systems provides an AI-infused perspective.
This might include two algorithmically recommended customer offers inspired by marketing analyst data and intelligent customer segmentation and campaign propensity scoring powered by Watson models.
Going further, feedback from the customer can then be entered and that data influences future offers because it goes right back into IBM’s data and AI platform. IBM Cloud Pak for Data, which helps to simplify data management and protect sensitive data by establishing a framework for implementing trustworthy AI.
Obstacle #3: Solving for the wrong “x.” In hundreds of conversations I’ve had with enterprise leaders over the years about AI, one common failure I see not identifying the right problem or identifying use cases that will yield high return from AI.
Opportunity: Clearly articulating the problem to be solved. With AI, we are talking about a machine making reasonable conclusions based on data. Better defining the problem is akin to asking better questions.
Imagine the difference if you were in a store and asked someone if they sold products. The question is too vague to expect a meaningful answer. Ask where the tomatoes are and you get a clear answer. Both are valid questions, but one is more focused. That’s how defining the problem should be (this is not just for AI purposes; I devote a lot of space in my book, Ascend Your Startup, to defining the customer problem because I believe building the wrong solutions plagues many companies)
In an interview with famed Mount Everest climber George Mallory, a reporter asked him why he wanted to climb the formidable mountain. His answer: “Because it’s there.” AI is very much the same thing. It has obstacles, yet it has the allure of opportunity and of making measurable progress.
Here are the big three takeaways for enterprise companies:
• Use a data fabric. Information is powerful – and it exists! Don’t let siloed data and inconsistent data formats hold people back from making better decisions.
• Give people what they need to succeed in their jobs. Tools such as low code/no code enable business users to rapidly leverage data and apply AI in their decision making.
• Go back to square one and define the problem. Solving for “x” without fully understanding “x” wastes precious time, causes unnecessary frustration and marginalizes the experience for everyone involved.
The Rise of AI
In a Forbes article on the topic of AI, author Manas Agrawal writes, “With rapid learning and adoption, AI is no longer a crystal ball technology but something that humans now interact with in nearly every sphere of life.”
In a very short time, we won’t be talking about AI adoption as people see it as part of doing business and part of making life more efficient. AI then will shift to being part of an enterprise’s business strategy, delivering value for non-technical people working in many different areas like customer experience, brand differentiation, HR, research and development, management and sales.
This is what the democratization of AI looks like at the crossroad of technology and humanity to improve outcomes for people leading successful enterprise businesses.
A Virtual Phone Number is an Essential Tool in the Digital Era
While it may seem as though our work needs us to be on call 24/7, there comes a time when there needs to be a line drawn between work and relaxation.
One of the key ways of achieving this while still providing a professional response to clients is through the use of a virtual phone number.
This article will explain exactly what a virtual phone number is and what it can do for you and your business.
What is a Virtual Phone Number?
When we think about a traditional phone, then it’s limited by a physical location; it needs to be plugged into the socket for it to work. Then when we consider mobile phones, you need to have a SIM card to make calls or receive messages.
A virtual phone has none of these limitations. You can still make and receive calls and messages, but it also provides the opportunity to configure how it operates to meet your needs.
Key Benefits of a Virtual Phone Number
We’ve identified six key benefits that will have you convinced of the need to get a virtual phone!
1. You Only Need One Phone
If you need two phone lines, then the traditional route would be to get a second phone and sim card. With that comes all the hassle of carrying them both around with you while also remembering to keep them both charged.
With a virtual phone number, you just need one phone. So that means you have your sim card and then an app for the second number. Then there’s the added advantage of being able to get a virtual phone number up and running in just minutes.
2. Take Calls From Anywhere
If you’re traveling overseas, then using your mobile phone can start to get really expensive both for you to receive the call and for the person calling you. With a virtual phone number, you don’t get that problem. Because the number isn’t associated with a physical phone, you can take calls no matter where you are in the world.
Now you could be sitting on the beach on vacation and still take calls without anyone knowing any difference! (though we strongly recommend getting some time off from work each year!)
3. Flexibility in Directing Calls
Remember what we said about trying to get some time away from work? Well, with a virtual number, you can quickly and easily direct calls to another member of the team.
4. Track Marketing Responses
When you have several marketing campaigns running at the same time, it can be difficult to accurately assess which one is giving you the best results. With virtual numbers, you could set up a different number for each campaign. Now you can answer calls knowing which campaign the caller is responding from, which means a far improved customer experience, and you can track your return on investment from your marketing spend.
5. Never Miss a Call
Not answering a call from a client is a sure-fire way of losing their business to the competition. A virtual number offers two different ways of avoiding that situation. First of all, you can set up the virtual number to be redirected to a group of other numbers at the same time. So that might mean that it goes to everyone in your customer service team.
The second option that comes with apps such as Chalkboard is to have an automated message response. So, when a call isn’t answered, the caller will be sent a pre-written autoreply message. While taking the call should always be the priority, there may be times when that can’t happen. This way, your client will still know that you value their call and that someone will come back to them.
6. Extra Functionality
With a landline phone its functionality is pretty much limited to making and receiving calls. With an app-based virtual number, you suddenly have access to a whole range of features to make life easier.
So, one feature that can be a huge time saver for small businesses comes the ability to automate the customer review process. Getting reviews can be hugely time-consuming, from asking customers to give feedback through to checking and responding to what’s been published.
A virtual app can automate the process of asking customers to provide reviews and then notify you every time a new review is published.
Why is Apple So Successful? Apple’s Machine Learning Strategy & Self Driving Car Project
Why is Apple So Successful? Apple’s Machine Learning Strategy & Self Driving Car Project
Apple is successful due to its high quality products, unique culture, loyal fan base, excellent customer service and highly skilled workforce.
The success of Apple goes beyond its simple products as it’s using the latest technologies including artificial intelligence, machine learning and deep learning.
Machine Learning (ML) is increasingly dominating the keynotes where Apple executives take the stage to introduce new features for iPhones, iPads, Macbooks and the Apple Watch. Machine learning enables systems to learn without being explicitly programmed. Based on algorithms and huge datasets for training, systems learn to recognize patterns that had not previously been defined. The acquired knowledge can then also be applied to new data.
Apple’s machine learning strategy centres around its devices. The company has positioned itself as a pioneer of in-device machine learning technology, with its superior security and potential for creating unique, user-engaging experiences.
Apple is targeting 2024 to produce a passenger vehicle that could include its own breakthrough battery technology.
What’s The History of Apple?
Source: Office Timeline
Apple Inc., formerly known as Apple Computer Inc., is a technology multinational company that has its headquarters in Cupertino, California.
The company specializes in designing, developing, and selling consumer electronics, computer software, and online services. Apple Inc. was founded by two young tech enthusiasts, Steve Jobs and Steve Wozniak.
The Macintosh, released in 1984, introduced the Graphical User Interface (GUI) to the mainstream. Apple’s second product, the Apple II was a huge success. It was the first personal computer to earn such mass-market success.
The company started struggling after it ousted Steve Jobs back in 1985. It was even on the verge of bankruptcy when Steve Jobs returned to the company in 1997.
Steve brought in some brilliant ideas and lifted the spirit of the company. He led the company to recovery by introducing the iPod in 2001, the iPhone in 2007, and the iPad in 2010. Apple earned a profit of almost $40 million in the 2014 fiscal year.
Steve Jobs died of cardiac arrest in 2011. Tim Cook, his longtime deputy, took over as the CEO and has presently been leading the company to success.
How is Apple Implementing Machine Learning?
Over the course of the last several years, Apple has bought startups such as Emotient, Turi, Glimpse, RealFace, Shazam, SensoMotoric, Silk Labs, Drive.ai, Laserlike, SpectralEdge, Voysis, XNOR.ai, and more, all with the aim of improving the artificial intelligence and machine learning capabilities of its products and services.
Apple’s machine learning approach addresses two major pain points: training of models on the cloud is expensive, and getting them to work on mobile devices is difficult at best.
The company has always stood for that intersection of creativity and technology.
Machine learning is a key component of Apple’s broader initiatives.
Apple’s machine learning strategy continues to be focused on running workloads locally on devices, rather than relying heavily on cloud-based resources, as competitors Google, Amazon, and Microsoft do.
Machine learning is very much a part of Apple’s overall strategy, and it is building it into the very fabric of its devices and services.
Uses Cases of Machine Learning in Apple Devices
Source: Log Point
By running on machine learning models on-device, Apple guarantees that the data never leaves the device of the users.
If Apple can provide tools that make it feasible to train models locally at reasonable speeds, it could further increase its hardware footprint because for individual developers training models locally on a single machine is much more cost effective than doing so on the cloud.
Here’s a quick recap of the more prominent machine learning features developed by Apple:
- Facial Recognition: Apple uses biometrics to map facial features from a photograph or video. Smart cameras can help identify your friends or family members in your photos.
- Native Sleep Tracking: This uses machine learning to classify your movements and detect when you’re sleeping. The same mechanism also allows the Apple Watch to track new activities.
- Handwashing: It not only detects the motion but also the sound of handwashing, starting a countdown timer to make sure you’re washing for as long as needed.
- Library Suggestions: A folder in the new App Library layout will use “on-device intelligence” to show apps you’re “likely to need next.” It’s small but potentially useful.
- Translation: This works completely offline, thanks to on-device machine learning. It detects the languages being spoken and can even do live translations of conversations.
- Handwriting Recognition: Apple is receiving and interpreting intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. Apple is doing a great job at image recognition tasks, and identifying both Chinese and English characters is a fitting challenge.
What Are The Key Drivers Behind Apple’s Success?
Apple’s success lies in a strategic vision that transcended simple desktop computing to include mobile devices and wearables. Both performance and design are key drivers of the Apple brand and its ongoing success.
A significant part of the credit for the iPhone’s success has to go to the App Store because the app ecosystem keeps customers coming back to Apple year after year when their mobile contracts are up for renewal, Apple has pushed developers to integrate AI into their third-party apps.
When Apple launched Siri, it became the first widely used natural language processing (NLP)-powered assistant.
Apple is also relying on a talented workforce. It is hiring the very best and most creative designers and engineers to build widely successful easy to use products.
Why is The Apple Car Project Still Secretive?
Hundreds of Apple employees are working on developing a self-driving Apple-branded car aimed at consumers.
Apple’s car project is still in the works. Deep integration with iOS is also expected. Apple is still waiting for autonomous testing permits received from DMV. A self-driving software is also being tested.
The company is also developing a new battery design that has the potential to radically reduce the cost of batteries and increase the vehicle’s range.
Apple is working on a monocell design that will bulk up the individual battery cells and free up space inside the battery pack by removing pouches and modules that hold battery materials. This will allow for more active material in a smaller package. The battery technology has been described as “next level” and similar to “the first time you saw the iPhone.”
Apple’s ultimate dream is to dominate every sector by leveraging machine learning. Developing a safe self-driving car represents a supply chain challenge for Apple, a company with deep pockets that makes hundreds of millions of electronics products each year with parts from around the world, but has never made a car. It’s important to bear in mind that it took Elon Musk’s Tesla 17 years before it finally turned into a sustained profit making car.
The Dark Side of Metaverse, Cryptocurrency & NFTs
Digital ponzi schemes promise high financial returns by exploiting breaches in the metaverse, cryptocurrency and NFTs.
Organisations that engage in a ponzi scheme focus all of their energy into attracting new customers to make investments.
Due to the lack of regulation in new technologies, fraudulent arrangements are premised on using new investors’ funds to pay the earlier backers.
What is a Ponzi Scheme?
A Ponzi scheme is a fraudulent investing scam promising high rates of return with little risk to investors. A Ponzi scheme is a fraudulent investing scam which generates returns for earlier investors with money taken from later investors. This is similar to a pyramid scheme in that both are based on using new investors’ funds to pay the earlier backers.
Ponzi schemes rely on a constant flow of new investments to continue to provide returns to older investors. When this flow runs out, the scheme falls apart.
The term “Ponzi Scheme” was coined after a swindler named Charles Ponzi in 1920. However, the first recorded instances of this sort of investment scam can be traced back to the mid-to-late 1800s, and were orchestrated by Adele Spitzeder in Germany and Sarah Howe in the United States. In fact, the methods of what came to be known as the Ponzi Scheme were described in two separate novels written by Charles Dickens, Martin Chuzzlewit, published in 1844 and Little Dorrit in 1857.
In 2008, Bernard Madoff was convicted of running a Ponzi scheme that falsified trading reports to show a client was earning a profit on investments that didn’t exist.
How to Spot A Digital Ponzi Scheme
Regardless of the technology used in a digital Ponzi scheme, most share similar characteristics:
- A guaranteed promise of high returns with little risk
- A consistent flow of returns regardless of market conditions
- Investments that have not been registered with the Securities and Exchange Commission (SEC)
- Investment strategies that are secret or described as too complex to explain
- Clients not allowed to view official paperwork for their investment
- The investment opportunity seems too good to be true
- Customers facing difficulties removing their money
The Dark Side of the Metaverse
Harassment, assaults, bullying and hate speech already run rampant in virtual reality games, which are part of the metaverse.
Most of metaverse’s envisioned activity will have a financial aspect, and much of it will be unburdened by today’s technological, regulatory, legal and even logical/ethical constraints, opening the doors wide to an army of fraudsters, large and small crime cartels, and cyber criminals, thieves, spies, blackmailers, and every vice in between. Both legit players and every type of criminal under the sun is sure to try to exploit the obvious benefits of anonymity coupled with the luxury of enhanced presence and access, enabling crooks to cast their malign nets far and wide in search for victims – the naïve, uninitiated, old, and impressionable.
Criminals will surely flock to the metaverse, since it promises to be a rich source of potential recruits, operational capabilities and funds. The metaverse will expand the reach and impact of online recruiters, help them to fine-tune their message to optimise impact and help them press with accuracy every button in their targets’ psyche.
State-sponsored and commercial intelligence outfits, including North Korea, and, of course, Meta, Amazon and Google, are already investing time and resources to learn about and probe the operational limits of the upcoming interface between reality and virtuality to collect more information about everyone and everything.
The Dark Side of Cryptocurrency
Scammers exploit the fact that so many people are still fairly unfamiliar with cryptocurrency aside from its supposed “get rich quick” potential.
Chainalysis reports that a single Ponzi scheme based in China by itself brought in at least $2 billion last year, which would make it one of the biggest ever. PlusToken was a cryptocurrency wallet service that promised users high returns if they used Bitcoin or Ethereum to buy the fake company’s own token, called Plus. An elaborate marketing campaign convinced more than three million people—the majority of whom were in China, Korea, and Japan—to invest by reaching them through the popular messaging platform WeChat, holding in-person meetups, and posting ads in supermarkets.
In June 2020, Chinese authorities arrested six people alleged to have been behind the scam, but it appears that at least one still hasn’t been caught, since someone has continued to launder the funds and even cash some of them out.
The Dark Side of NFTS
To buy your first NFT, you’ll need to sign up for a wallet that transacts on the Ethereum blockchain. MetaMask is perhaps the most popular Ethereum wallet for NFT collectors. However, MetaMask users were recently targeted in a phishing scam involving phoney advertisements that asked for users’ private wallet keys or 12-word security seed phrases (a big red flag). There are also fake malicious pop-ups operating via Discord, Telegram and other public forums that link to normal-looking login pages, such as MetaMask or other popular websites.
Non-fungible tokens (NFTs) have exploded into a multibillion-dollar sector of the crypto industry in the last 12 months alone. Top collector’s items, such as rare pieces from the Cool Cats and Bored Ape Yacht Club collections, trade for upwards of $30,000 or more.
If five- and six-figure price tags seem like a lot for a JPEG, NFT creators have a one-word answer for you: utility. Because NFTs create an indelible digital record of your ownership on the blockchain (aka the same tech on which crypto is minted), owning a digitally tokenized piece of art can also serve as your membership ticket to exclusive online clubs, gaming communities, Discord chat rooms and interactive experiences.
Regulation is needed to protect the reputation of the metaverse, cryptocurrency and NFTs markets.
It also protects investors from fraud, ensuring fair competition and a level playing field. Good regulation is also good for the tech industry.
The metaverse, cryptocurrency and NFTs are still revolutionary. Patience is needed as these technologies have the potential to change the world forever.
Given the rapid rate of technological change, professional financial advisors skilled in cryptocurrency, NFTs and the metaverse are required. That way, investors can understand technological, financial and cybersecurity risks to make informed decisions.
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