ChatGPT was created by the technology company OpenAI. Microsoft is now testing the AI language as an enhancement to its Office suite of products, including Word, Outlook and PowerPoint.

OpenAI has had a lot of press lately given that their AI products ChatGPT and Dall-E 2

For their ability to produce text and images, OpenAI's intuitive technology products, such as ChatGPT and Dall-E 2, have become online sensations. Many people have made assumptions about the ethical and practical use of ChatGPT. Microsoft is attempting to make better use of the company's AI models, though. According to The Information, the business has already updated its autocomplete feature with a version of the OpenAI GPT text-generator model.

Microsoft has been testing GPT AI model functionality in Outlook and PowerPoint. These include tools that enable users to locate Outlook search results in email inboxes using speech-like AI-driven commands rather than keywords. Additionally, AI models for Outlook and Word will propose email responses or offer edits to documents to improve writing abilities. There is no information yet on whether this use will eventually be incorporated into consumer-facing versions of Microsoft Office or if the company is merely exploring the GPT model's potential.
​
Microsoft invested $1 billion in OpenAI in 2019, and according to the report, "acquired an exclusive license to the underlying technology behind GPT-3 in 2020," before this practical application of the GPT technology.
Microsoft may aim to integrate the GPT AI model into its Bing search engine besides its Office suite to compete with Google. According to The Verge, this could be the item that is most likely to be released, with a potential release date of March.

Making data-driven decisions is becoming more and more understood by companies in every industry as a requirement for competing today, in the next five years, in the next twenty, and beyond. According to current market research, the worldwide artificial intelligence (AI) market will "increase at a compound annual growth rate (CAGR) of 39.4% to reach $422.37 billion by 2028," driven by the exponential expansion of unstructured data in particular. The era of data overload and AI has arrived, and there is no turning back.
This reality implies that AI can truly sift and handle the deluge of data–not just for big giants like Alphabet, Microsoft, and Meta with their massive R&D departments and tailored AI tools, but for the typical corporation and even some small and medium-sized businesses.

Well-designed AI-based systems quickly filter through enormously vast datasets to produce fresh insights, which fuel fresh sources of income, adding significant value to enterprises. But without the new kid on the block, vector databases, none of the data expansion really becomes operationalized and democratized. Vector DBs represent a paradigm shift in database management and a new category for using the exponential amounts of unstructured data that are currently untapped in object stores. In particular, vector databases provide a mind-numbing new degree of search capacity for unstructured data, but they can also handle semi-structured and even structured data.

Vectors and Search. Unstructured data, which can't be simply sorted into row and column relationships, rarely matches the relational database paradigm. Examples include photos, video, audio, and user actions. Unstructured data management methods that are incredibly time-consuming and unreliable frequently include manually labelling the data (think labels and keywords on video platforms).

The real problem is that human methods make it very hard to perform a semantic search that comprehends the context and meaning of a picture or other unstructured piece of data, in addition to a search query.
Enter embedding vectors, often known as feature vectors, vector embeddings, or just embeddings. They are numerical values, or sort of coordinates, that represent unstructured data features or objects, such as a part of a picture, a section of a person's purchasing history, a few frames from a video, geospatial information, or anything else that doesn't neatly fit into a relational database table. These embeddings enable scalable, snappy “similarity search.”

Quality Data and Insights. An AI model, or more precisely, a machine learning (ML) or deep learning model, trained on very large amounts of high-quality input data, produces embeddings as a computational byproduct. A model is the computational result of an ML algorithm (method or procedure) conducted on data, to further draw crucial distinctions. Sophisticated, widely used algorithms include STEGO for computer vision, CNN for image processing and Google’s BERT for natural language processing. The resulting models turn each single piece of unstructured data into a list of floating-point values—our search-enabling embedding.

Therefore, a neural network model that has been properly trained will produce embeddings that are consistent with particular content and may apply to a semantic similarity search. A vector database, specifically designed to manage embeddings and their unique structure, is the instrument to store, index, and search through these embeddings.

The fact that developers from everywhere may now incorporate a vector database into AI systems, with its production-ready features and lightning-fast unstructured data search, is crucial in the industry.
​
Organizationally, a crucial component of standardizing the usage of vector databases is assisting business teams and their leadership in understanding why and how they can benefit. The concept of vector search has been around for quite a while, but only on a very small scale. Many businesses aren't really accustomed to having access to the kind of data mining and search capabilities that contemporary vector databases provide. Teams sometimes struggle with knowing where to begin. Therefore, their creators continue to place a high focus on spreading the word about how they operate and why they are valuable.

Walmart was late in actively addressing competition with Amazon in ecommerce. In its new efforts to meet Amazon’s challenge, the company announced that it has plans to open four new fulfillment centers.

These fulfillment centers are where online orders will be packed and shipped. They are the first of a new breed of logistics for Walmart. The technology-heavy investments they are making involve robotics, machine learning and artificial intelligence.

Walmart said they were working with Knapp, an international logistics provider, to replace their current 12-step manual process with a new 5 steps and doubling the number of orders a location can fulfill in a day. Instead of moving product with people, the new approach will have robots shuttle skiffs to stagers directly, eliminating the need for floor personnel to walk up to nine miles or more a day.

"These four next-generation [fulfillment centers] alone could provide 75% of the U.S. population with next- or two-day shipping on millions of items," David Guggina, Senior Vice President of Innovation and Automation at Walmart U.S., wrote in a blog post.

When the four new centers join the company’s existing 31 dedicated e-commerce fulfillment centers, Walmart believes it will be able to reach 95% of the U.S. population with next- or two-day shipping. And with its 4,700 physical stores, the company could offer same-day delivery to about 80% of the U.S.

Both Walmart and Amazon are focusing on their weaknesses as compared to each other. Walmart is spending billions on logistics and automation and Amazon is spending billions on new physical stores, particularly for groceries (where Walmart dominates.)

Walmart’s new fulfillment centers will be located in Joliet, Illinois; McCordsville, Indiana; Lancaster, Texas; and Greencastle, Pennsylvania—with each planning to hire over 1,000 new workers. 
​
Amazon currently has 253 fulfillment centers, 110 sortation centers, and 467 delivery stations in North America, not to mention hundreds of thousands of drivers and over 100 Amazon Air cargo aircraft at the end of 2021.

The single most important factor in surviving a battlefield injury is the combat medic. The medic is first on the scene and can administer help within the Golden Hour or even Golden Ten Minutes. Quick, effective medical procedures can be the difference between life and death.

The Defense Advanced Research Projects Agency (DARPA) has selected Raytheon BBN to lead a team to develop an augmented reality device that will provide the combat medic with a virtual assistant. The system will use a set of AR goggles, which will provide visual information on 50 different medical procedures.

Medics are highly trained in the most common battlefield injuries, but they aren’t doctors or surgeons and often have no experience in little-used procedures which may be needed at a moment’s notice. This is why DARPA is working on its Medical Assistance, Guidance, Instruction and Correction (MAGIC) system. 

MAGIC uses a pair of augmented reality goggles equipped with audio and video sensors and special artificial intelligence software that can act as an assistant to monitor the situation and advise the medic on how to proceed.

Raytheon will use machine learning technology to ‘teach’ the system both medical skills and situation assessment skills. The initial prototype will study 2,500 stereo videos and almost 50 million images. The machine learning process will review the historical data and synthesize useful concepts and solutions from that data.

When the AI software is ready, MAGIC should be able to provide spoken suggestions to medics or project visual overlays on the scene to guide their hands through needed medical procedures. The system will also provide events timing from engagement to final hand-off to field hospital personnel. MAGIC will also provide dosage guidance for in-field medications.

A first prototype is expected in about 18 months.
​
"The combat medical environment is challenging and chaotic," said Raytheon BBN scientist Brian VanVoorst. "Our goal for the Raytheon BBN MAGIC AI tool is to help support personnel to provide guidance as needed without disrupting their concentration."

Edge technologies are projected to experience the highest investment increase in 2022, growing 76% to $462,000 according to a new Gartner, Inc. survey.

Looking back to 2021, 5G drew the highest average investment in 2021, with survey respondents reporting an average of $465,000 invested in the technology. This was followed by IoT at $417,000 and edge technologies (i.e., edge AI and edge computing) at $262,000. 

The edge architecture is allowing AI to become more actionable. Moving AI inferencing closer to the point of data generation is making the edge more intelligent, which is improving decision-making within organizations and data-driven outcomes. In conducting AI inferencing on the edge, the data is processed in real time to generate actionable insights for decision-makers.

It makes sense that among the reasons organizations are using edge technologies and 5G are to improve employee productivity, augment existing products and services by making them more connected and intelligent, and automate business processes. Asset intensive industries such as manufacturing, natural resources and energy are among the early adopters of ET to solve core business problems. 

It’s interesting to note that the decisions to invest in these emerging technologies (ET) no longer solely rest with IT. The survey showed that Boards of Directors are among the main decision makers for ET investments in over half of surveyed organizations, just behind CIOs and CTOs, signaling that the business has more confidence in the ROI these technologies bring.

In fact, counter to conventional thinking, most survey respondents reported that ET investments are meeting or exceeding user expectations. When enterprises are choosing between two vendors’ ET offerings, the vendors’ ability to provide demonstrable use cases and a track record of success ultimately clinches the win. Product managers should look to focus on these success stories and ensure short deployment/ implementation timelines when targeting business users to adopt ET.
​
Gartner surveyed 500 global respondents from mid-sized and larger organizations in September and October 2021 to understand buying behavior when investing in emerging technologies.

Predictions indicate that a nanometer-sized wave-based computer could solve equations in a fraction of the time of their larger, electronic counterparts.  
Booting up your laptop may seem like an instantaneous process, but in reality, it’s an intricate dance of signals being converted from analog waveforms to digital bytes to photons that deliver information to our retinas. For most computer uses, this conversion time has no impact. But for supercomputers crunching reams of data, it can create a serious, energy-consuming slowdown. Researchers are looking to solve this problem using analog, wave-based computers, which operate solely using light waves and can perform calculations faster and with less energy. Now, Heedong Goh and Andrea Alù from the Advanced Science Research Center at the City University of New York present the design for a nano-sized wave-based computer that can solve mathematical problems, such as integro-differential equations, at the speed of light.
One route that researchers have taken to make wave-based analog computers is to design them into metamaterials, materials engineered to apply mathematical operations to incident light waves. Previous designs used large-area metamaterials—up to two square feet ( ∼0.2 m2)—limiting their scalability. Goh and Alù have been able to scale down these structures to the nanoscale, a length scale suited for integration and scalability.
The duo’s proposed computer is made from silicon and is crafted in a complex geometrical nanoshape that is optimized for a given problem. Light is shone onto the computer, encoding the input, and the computer then encodes the solution to the problem onto the light it scatters. For example, the duo finds that a warped-trefoil structure can provide solutions to an integral equation known as the Fredholm equation.
Goh and Alù’s calculations show that their nano-sized wave-based computers should be able to solve problems with near-zero processing delay and with negligible energy consumption.

​

Here are a few FinTech firms that are emblematic of how technology innovation is shaping the future of money across a variety of financial services.

Versapay—Accounts receivable
In its quest to rid companies of paper checks, Toronto-based Versapay has developed one of the first collaborative accounts receivable platforms, combining advanced invoicing, automation, and payments technology to deliver improvements and efficiencies for its 250,000 business clients. It has mushroomed during the pandemic, which fueled a demand for digital payments, including the digitizing of accounts receivable services. In a recent interview, Versapay CEO Craig O'Neill said a large majority of finance leaders will adopt digitized payments in the next few years. "Surveys show that 93% of finance leaders are saying they're going to digitize how they do business on both the accounts payable and accounts receivable side. There's going to be a sea change over the next couple of years, which will really change the face of business: paper goes away completely, including check stock, and will set people free to work wherever they want," he said.  

BestEgg—Personal lending and financial management resources
This Wilmington, Del.-based FinTech, is on a mission to help people feel more confident about their everyday finances by making money accessible through fast, simple, and easy personal loans. BestEgg has created a proprietary AI platform to deliver digital products including loans for debt consolidation, credit card refinancing, home improvement, moving, vacation and baby adoption. BestEgg recently introduced an online financial health platform to bolster confidence for consumers in the wake of the COVID-19 pandemic, which has had a debilitating effect on many peoples' finances. "The future of digital finance is human," says Sabrina DeVito, chief strategy officer for Marlette Holdings, which operates BestEgg. "Digital can come across as cold, but the winners are going to be those who not only use digital to create extraordinary experiences but also are human in how they come across," she said.

Kabbage—Small business financing
Small businesses took a beating during the coronavirus pandemic—large numbers shut down permanently, crippling local communities both economically and socially. Fortunately, more small businesses are forming in the shadow of the pandemic, and one FinTech is helping to make that possible. Kabbage, backed by American Express, is a one-stop FinTech resource for small businesses, providing access to cash flow solutions such as business checking, flexible funding, and payment processing services. It recently launched Kabbage Funding, which provides flexible lines of credit between $1,000 and $150,000, and Kabbage Checking for online business checking accounts.
"We saw a technology opportunity; a way to use an API to automate serving a business that couldn't have been served previously," said Kathryn Petralia, co-founder of Kabbage. Petralia says what she's seeing with small businesses is that there's more support by many other companies to serve these unserved small businesses. "They're a vital part of the economy—a vital part of job growth and so now we can do that using technology where we couldn't do it before because it was always hard to market to small business. But thanks to the data and these new technology platforms, we can do it," Petralia said.

UpTip—Cashless tipping
This new startup replaces cash tipping with an e-payment platform and enables users to provide ratings on service providers such as wait staff and limousine drivers. Companies are given a QR code after registering with UpTip. Customers scan the code on their smartphone to view the company's profile, give a star rating, leave feedback and, of course, provide a tip. Tips are deposited directly into a debit card or bank account of choice.
​
The travel and hospitality industry is still struggling in the wake of COVID and so are the people whose lives and livelihoods have depended on tipping. Eric Plam, UpTip's founder, believes the pandemic has been a catalyst to help people figure out how much they value people who serve them and the services they provide. UpTip will help collect the data used to determine how much service staff are valued. "We really believe that personality, enthusiasm and merit should be rewarded and so this is one reason we enjoy tipping and that's one thing we do with our service and ratings platform—we try to raise people who are doing a great job because we want to help them advance in their careers", Plam said.

SeaTable is software for combined information management. The software allows data of different types to be collected, prearranged, and analyzed. It uses an in-built spreadsheet web interface with customizable web forms for its data entry. 

SeaTable offers over 20 different column types, sorting, filtering, and grouping for its data organization. Besides the charts and pivot tables as traditional evaluation tools, it also offers application-oriented visualizations, including map views, gallery, Gantt charts, and Kanban.

The SeaTable software is available in 3 different versions, including on-premises, dedicated, and cloud variants. The on-premises variant is designed for large and medium-sized companies that don't want to outsource their data but prefer to store their own data center. Besides this, it offers a spreadsheet solution for teams that want flexible ways of working on projects, tasks, and ideas.

The spreadsheet solution doesn't limit users to texts and numbers; it also captures all information and stores documents, emails, URLs, check boxes, images, drop-down lists, etc. According to the CEO & Founder, Christoph Dyllick-Brenzinger, "SeaTable generates enormous added value for teams that want to work together on ideas, tasks or projects. With SeaTable, you have all your data in one place and can access it flexibly from anywhere. Since our launch in July 2020, we have developed rapidly. We generate many new customers every day who have the goal of working in a structured and, above all, more efficient way."

SeaTable Offers Unique Use Cases
SeaTable has different use cases. It can work as a bug, survey software, tracking system, project management tool, archiving solution, and a collaboration platform. Therefore, everyone, including agencies, developers, content creators, project managers, and market researchers, can use SeaTable to consolidate their project details.

The software can work as a collaborative database application. It offers advanced functions beyond what traditional spreadsheets can do. As a web application, it doesn't require any program installation, download, or configuration. All users require is to register on the platform and get their data organized.

"Over 100,000 downloads via Dockerhub and high customer satisfaction speak for our first-class and innovative software. With the development of SeaTable, it is always important to us to offer our spreadsheet solution across as many areas and industries as possible. Our spreadsheet & database hybrid supports many businesses around the world every day, either in the cloud or in their own data center," said Philipp Braun, CMO at SeaTable GmbH.
​
As a collaborative tool, it allows the exchange of data with third parties in several ways. Through efficient management and organization, the collaboration between customers and the team is well facilitated and optimized. It allows users to switch to five languages, including English, Russian, Chinese, German, and French. Versions are available from free to €148 per year.

AR has populated science fiction, specialized industries, and high-tech experiments for decades. But in the 2010s, major companies—and countless startups—began treating AR headsets as a potential mass-market platform.

Facebook founder Mark Zuckerberg predicted in 2017 that televisions and phones would be replaced by holographic glasses. Apple CEO Tim Cook called AR “a big idea, like the smartphone.” Microsoft envisioned people watching the Super Bowl in its HoloLens headset. Google launched its ambitious Glass platform as a potential successor to phones, then helped propel the AR startup Magic Leap toward billions of dollars in investments. More recently, telecoms have partnered with AR companies like the Chinese startup Nreal, hoping high-bandwidth holograms will create a demand for 5G networks.

These companies’ products—as well as those of other major players, including Snap, Vuzix, and Niantic—often look very different. But most of them promise a uniquely powerful combination of three features.

• Their hardware is wearable, hands-free, and potentially always on—you don’t have to grab a device and put it away when you’re done using it
• Their images and audio can blend with or compensate for normal sensory perception of the world, rather than being confined to a discrete, self-contained screen
• Their sensors and software can collect and analyze huge amounts of information about their surroundings—through geolocation and depth sensing, computer vision programs, or intimate biometric technology like eye-tracking cameras

Over the past decade, nobody has managed to merge these capabilities into a mainstream consumer device. Most glasses are bulky, and the images they produce are shaky, transparent, or cut off by a limited field of view. Nobody has developed a surefire way to interact with them either, despite experiments with voice controls, finger tracking, and handheld hardware.

Despite this, we’ve gotten hints of the medium’s power and challenges—and even skeptics of the tech should pay attention to them.

In 2016, for instance, millions of people fell in love with the phone-based AR game Pokémon Go. When players logged on to catch virtual monsters, many discovered parts of their neighborhoods they’d never thought to visit. But they also found a world built on data that placed more gyms and PokéStops in white and affluent neighborhoods. They forged in-person connections by sharing virtual experiences, but those connections could also allow for real-world harassment.

The effects went beyond people who played the game. The owners of some homes near Pokémon Go gyms experienced a sudden influx of trespassers, leading a few to sue its developer Niantic and secure tweaks to the game’s design. Public memorials like the US Holocaust Museum asked players to respect the space by not chasing virtual monsters into it. Even this early foray into augmented reality exported some of the internet’s biggest flaws—like its ability to collapse context and overwhelm individuals with its sheer scale—into physical space.

Writer and researcher Erica Neely says that laws and social norms aren’t prepared for how AR could affect physical space. “I think we’re kind of frantically running behind the technology,” she says. In 2019, Neely wrote about the issues that Pokémon Go had exposed around augmented locations. Those issues mostly haven’t been settled, she says. And dedicated AR hardware will only intensify them.
​
Smartphone cameras—along with digital touchup apps like FaceTune and sophisticated image searches like Snap Scan and Google Lens—have already complicated our relationships with the offline world. But AR glasses could add an ease and ubiquity that our phones can’t manage. “A phone-based app you have to actually go to,” says Neely. “You are making a conscious choice to engage with it.” Glasses remove even the light friction of unlocking your screen and deliberately looking through a camera lens.

Office 365 users are now in cybercriminals' crosshairs in a new phishing campaign, according to a warning the Microsoft Security Intelligence (MSI) team issued via Twitter. Malicious actors are using email addresses that appear to be legitimate, with display names that mimic bona fide services to dodge email filters.
Microsoft cautioned cybercriminals are going above and beyond to use detection-evasion techniques that are convincing and authentic-looking.

"An active phishing campaign is using a crafty combination of legitimate-looking original sender email addresses, spoofed display sender addresses that contain the target usernames and domains, and display names that mimic legitimate services to slip through email filters," MSI explained on Twitter.

The deceptive phishing campaign targets Office 365 organizations with employees who often send attachments to co-workers. MSI found phishing emails that seemed as if they were sent from a trusted source. Many of these emails contained faux Microsoft SharePoint attachments with labels such as "Price Books," "Bonuses" and "Staff Reports."

The phishing emails use a tactic called "typosquatting," which involves registering deliberately misspelled domains that, at first glance, look close to a well-known brand. Most quick readers would overlook the subtle typo.

If users fall for the bait and click on the "Open" link, it will lead them to a page that lures them to type in their Microsoft or Google credentials. According to MSI, these sign-on pages look very convincing, making users believe that they're on a trustworthy path to a legitimate website.

MSI kept emphasizing how authentic these phishing emails looked. Employers may not be able to rely on their employees' good judgment to identify suspicious-looking emails. That's why MSI shamelessly plugged its Microsoft Defender for Office 365 program as a solution, adding that this software "detects and blocks" these emails.
​
Phishing attacks are a huge thorn in the side for many popular companies like Netflix and PayPal, but the Redmond-based tech giant should be particularly concerned. According to a CheckPoint Research study, Microsoft topped the list as being the most imitated brand for phishing attacks.