Illustration: Felix Sockwell
By many measures, the Web is a phenomenal business success. Entire industries, such as auctions, book selling, travel reservations, news dissemination, and classified advertising, have undergone sea changes because of it. Web companies like eBay, Amazon.com, Yahoo!, and Monster are huge enterprises. Some of them are even turning a profit.
And yet the Web has also been an abject failure, at least so far. According to one estimate, e-commerce revenue for 2002 was a paltry 3.2 percent of all U.S. commercial transactions (about US $72 billion out of $2.25 trillion). Briefly high-flying companies like Priceline.com continue to disappoint investors. And Time Warner has dropped its Internet half, AOL, from its name, perhaps as a precursor to severing the company itself.
Even within the industries that the Web is coming to dominate, such as travel bookings and music sales, there’s so much more that could be done. The reason is, in a word, databases: dusty, musty databases filled with useful data that would be far more useful if linked with other, equally dusty databases; enormous databases that are locked up inside ancient mainframes and quaintly archaic minicomputers; lonely databases residing on specialized file servers throughout an enterprise; even modern databases on Web servers, all dressed up and ready to go, but stuck in long-obsolete proprietary formats or accessible only through hypermodern scripting languages.
Second-generation e-commerce will depend on unlocking those databases. And it’s starting to happen, thanks to a combination of modest technologies that together go by the name of Web services. Web services are a way programmers can make their databases available across the Web, let other programmers access them, and tie these disparate databases together into services that are novel, perhaps even wonderful. Travel agents could put together vacation packages from various airlines and hotel chains, extracting the best seasonal discounts automatically. A Web servicesbased wedding registry wouldn’t be limited to just one department store.
Beyond these consumer applications, over the next few years Web services will spur a transformation—albeit a quiet one—within a number of much bigger industries, including some, like insurance and banking, that haven’t really had a kick-start since the 1964 introduction of the IBM 360 mainframe.
Web services already are brushing the cobwebs off some of those old databases. Take, for example, air travel, an industry whose databases have always been a bit more open than most, because of the need to interact with independent travel agents. Until recently, the Internet had changed air travel only in tiny ways. For example, you could book your ticket online and then walk up to the check-in counter with nothing more than a printout of an e-mail message. But little else about the check-in process—including its long queues—had changed much in 30 years. Now, though, new self-service check-in kiosks are turning up, based, as it happens, on Web services software [see illustration, “Web Services Fly High”].
The kiosks let you identify yourself by, say, swiping a credit card or entering your e-ticket information. You can confirm your flight itinerary, and then some surprising screens appear. For example, there’s a seating chart, with your previously assigned seat highlighted in yellow and still-available seats in green. If you see one you’d prefer, just touch it with your finger—voilà, that’s your new seat assignment. At the end, you print out a boarding pass and head for the gate. Unfortunately, the new software can’t do anything about that line.
What has changed? The airline has opened up its databases to organizations and the public, showing you data hitherto seen only by airline personnel and travel agents. There’s something almost sinfully pleasurable about reviewing an airline’s seating chart, as if you were able to look over the shoulder of the airline agent at the counter. But what has changed behind the screen? The differences are a new layer of software—Web services—which link the airlines’ seating database to the easy-to-navigate kiosk interface.
Web services aren’t just about pouring old database wine into new bottles. They take databases—new ones as well as old—and place them in modern, easy-to-use decanters, whether it’s a Web browser, a custom application, such as the airline check-in kiosk, or just plain e-mail. Web services open these closed database applications and let them breathe. Once opened, a database can be used by other departments within an enterprise, vendors and customers, and, as in the airport kiosk, the public at large.
Travel is changing in other ways than the airport check-in. Galileo, the giant on- and off-line travel service from Galileo International Inc., in Parsippany, N.J., is using Web services to streamline back-office operations. For instance, it’s consolidating the large number of individual processes involved in a single transaction like buying a ticket or claiming a frequent flyer award. One of Galileo’s main goals is appealing: to do a better job of combining flights with empty seats, hotels with empty rooms, and rental cars sitting in garages into last-minute vacation specials. If it works, it could make a lot of money for the airlines, hotels, and car rental agencies—and for Galileo, of course.
Everything from online shopping and auctions to financial services stands to benefit. Take, for example, the insurance industry, a strong contender in the oldest-and-mustiest-database competition. For years and years, Joe Salesguy has paid Jane Customer a call, scrawling his notes on little slips of paper. Eventually, Joe goes back to the office, spills out all the paper onto a desk, and updates his prospective-client lists. Instead, a Web service could let him update the database with a text message from his cellphone. Not only would he save the trip to the office, but his manager would also be able to see Joe’s day progressing in real time. When Joe finally makes the sale, he would be able to create a new account record just minutes later, using an ordinary Web browser on his laptop or at an Internet cafe.
Having finally bought a policy, Jane might want to arrange a direct monthly payment from her bank to the insurance agency. Web services could come into play yet again, as long as either of those financial institutions, the bank or the insurance company, opened up its database to the other (with proper security built in, of course). If several insurance companies opened up their catalogs of insurance policies to Web services, an independent insurance agent could write a software application that compared them and helped customers like Jane find the best policy for their needs.
The insurance companies wouldn’t even need to be aware that such a third-party application had been created, because the Web services application would pluck data from publicly available databases found through the insurance companies’ Web sites. No wonder Dmitri Tcherevik, vice president of Web services management for Islandia, N.Y.based Computer Associates International Inc., predicts that financial institutions will be “the biggest beneficiaries of Web services.”
Many customers will prefer the new system, whether they’re checking in at the airport more quickly or updating a sales contact database while waiting for a double latte at a Wi-Fienabled coffee shop. But in addition to making users happy, Web services also dramatically drive down costs. Ron Schmelzer, a senior analyst for ZapThink LLC in Waltham, Mass., a Web services consulting firm, says that he’s seen software project costs cut by 90 percent because of Web services.
The largest savings come from reusing software from project to project. Think of an application, such as selling an insurance policy, as consisting of several layers. The user interface, at the top, will be different for a PC or a cellphone. The bottom layer, in which data is extracted, will differ for each database being drawn from. But the entire middle layer, in which the data is processed and prepared for presentation to the user, can be essentially the same. “The greatest integration costs are in middleware,” says Schmelzer.
The consequences—reducing costs, adding revenue from last-minute sales, and having better back-office operations, more-efficient salespeople, and happier customers—may be even greater than the changes wrought a decade ago when businesses first started using the Web to augment their sales, inventory, and other systems.
Take e-commerce, which has matured, but only up to a point. Sure, we can now get real-time bank account balances, transfer money between accounts, and pay bills; we can shop with online catalogs and track our purchases from the privacy of our homes. But almost everything we do on the Web today, including Google searches, catalog shopping, and looking up driving directions, can be done only with a human sitting in front of a screen.
Nevertheless, we’ve certainly progressed from the pre-Internet era. What we’ve built is, in fact, the foundation for the new world of Web services. In 10 short years, Web browsers have liberated us from the tyranny of specific hardware and the near-monopoly of the Windows operating system. Internet Explorer, Netscape, Opera, Safari—they all display Web pages in more or less the same way, regardless of the platform they’re running on: Microsoft Windows, Mac OS 9 or X, Linux or Unix. That’s because of two things: the Hypertext Transfer Protocol, which provides a standard for the way Web pages are downloaded from a Web site to a computer, and the generic nature of Web pages themselves.
Once a page is coded in Hypertext Markup Language (HTML), a browser knows just how to display it, with markup codes specifying fonts, heading styles, columns, table structures, location on the screen of a graphical image, and so on. But HTML was designed to encode things that will be viewed by people, rather than processed by another machine. HTML mixes formatting commands (such as color and positioning) with data (the text itself, graphics, sounds, and so on), because it was designed as a display language.
Ferreting through HTML to retrieve embedded nuggets of data while simultaneously ignoring formatting constructs isn’t impossible, but it’s unnecessarily difficult. This parsing task is complicated by the fact that HTML code is not static. It changes, for instance, whenever a Web site owner changes the appearance of a Web page. Some changes are built into a Web site’s very design: a bank customer, for example, will see a different screen depending on whether or not there are enough funds to cover the withdrawal. Furthermore, HTML coding often contains errors; those mistakes can easily trip up a parsing program.
So if Web services are to build powerful networks of collaborating databases and services, the first step is replacing HTML with something more compatible with the world of databases, something that can be understood by another computer. And such a new language has been developed. It’s a superset of HTML, called XML, for Extensible Markup Language.
XML is a universal standard for representing data, so XML-based programs are inherently interoperable. Basically, XML uses the lowest common data denominator available, which is text. Here’s how it works: data in XML form is consigned to specific fields. There might be one field for “price,” for example, and another for “quantity.”
Once information is in XML form, it can be extracted from different databases and compared, so long as the two databases have equivalent fields, such as price and quantity. But what if the databases have fields that are similar but not equivalent? It would be a problem today, but perhaps not tomorrow. Emerging Web service innovations would add extra data, called metadata, that would let a database “announce” its structure. Then two different databases with similar fields could be compared by a software program with no human intervention at all.
Since Web services are used to create interoperable Web applications, there must be some mechanism to move XML data across the Internet. The easiest way would be to take advantage of an already existing protocol, the obvious candidate being the Hypertext Transport Protocol—the ubiquitous “http” part of a Web address. But HTTP was designed to move HTML data.
For an Internet connection to transport XML instead of HTML for a Web service, a new mechanism was needed to allow XML data to piggyback on HTTP messages, the means by which Web sites receive commands from the keyboards of surfers and transmit data back for display. That mechanism is a new standard, Simple Object Access Protocol (SOAP), developed by independent programmers in conjunction with researchers at Microsoft Corp., in Redmond, Wash.
The SOAP standard was invented to “overlay” XML over HTML in a commonly understood way. SOAP acts as a generic wrapper for transmitting bits of data. It’s a kind of envelope that doesn’t know what’s inside but is recognized and accepted by Web browsers and servers.
Together, XML and SOAP give Web service applications unparalleled interoperability. In fact, in principle, a Web service can be written to use databases that the application developer didn’t even know existed. That’s a tremendously useful feature for certain applications—for example, older mainframe applications that can be given a new lease on life through a Web services interface. Imagine a freight forwarder writing an application that accessed a musty U.S. Customs database of commodities, so that an importer could figure out his or her customs duties before making a shipment.
For such an application to work, Web sites have to be able to announce to the service that they contain data—such as clearinghouse information, commodities listings, or an airline schedule—that might be useful to it. So another specification was developed: Universal Discovery, Description, and Integration (UDDI).
Basically, UDDI lets Web services look for databases in the same way that Google lets humans look for Web pages. One way that’s done is through UDDI registries, a Yellow Pageslike directory in which companies list their businesses and the Web-related services they provide. IBM and Microsoft in the United States and Germany’s SAP are among the companies that maintain UDDI registries.
Using a search engine, of course, is sometimes a hit-or-miss proposition—you try different Web pages until you find one that has the information you need. That doesn’t work so well without a human to make those judgments. Thus, one more standard had to be invented: the Web Services Description Language, or WSDL.
This standard allows a machine to figure out on its own just what’s at a site once it’s been identified. A program accessing a Web service retrieves a WSDL description from the service. The description itself is specially formatted XML data telling the prospective user the procedures it can call and a little bit about them. UDDI and WSDL are a magnet that works much better than a human when it comes to finding a needle in the haystack that is the Web [see table, “Enhancing the Traditional Web”].
All these new protocols, SOAP in particular, took years to develop. Indeed, they’re still works in progress, in part because contributing companies want to receive patent royalties or just don’t want a competitor to control a standard. Those same concerns sabotaged two earlier transport mechanisms, one from the Unix world and one invented by Microsoft.
These attempts failed because they didn’t provide the freedom Web interactions need—the casualness of vehicles and highways, where anything from a motorcycle to an 18-wheeled truck can travel on any road and go almost anywhere. In other words, any Web client program running on a server, a PC, or even a PDA or Web servicesenabled cellphone, can establish communication with any Web service on the fly. This property is known as delayed binding. Traditional applications, on the other hand, will stop working when a change is made in one component, such as adding one more parameter to a procedure, if this change is not propagated to the rest of the software system.
The practical impact of delayed binding is enormous. Because of WSDL, a program calling a Web service can check the configuration of the Web service as the program runs, allowing the calling program to adjust for any changes that may have occurred in the Web service. This lets programmers separately develop and test the different components of an application, which will continue to run correctly even if one of its constituent modules is upgraded.
The ability to change one part of an application without having to revalidate the whole system radically reduces development costs. Because of these savings, we’ll soon see Web services even in complex business operations, such as the processing of insurance quotes or mortgage loans, where different parts of the process can reside in different organizations or companies and run in a variety of computer architectures. The loose coupling and delayed binding properties of Web services will let companies gradually replace older software and interfaces without the disruption of massive software upgrades.
Of course, benefits like these come at a price. There are extra run-time checks, and the text-based data used in XML makes it inefficient. So applications using Web services are several times slower than applications using binary data. In addition, sending plain-text XML across the open Internet makes it vulnerable to security breaches. A number of research efforts are addressing these shortcomings.
However immature, Web services are providing tremendous benefits today. When you add Web services to a classic database, such as a large retailer’s inventory, unexpected and delightful applications emerge. Take Amazon.com, one of the largest databases to be opened up to Web services. For almost a decade now, affiliated companies and weekend programmers have been experimenting with it incessantly. Many of the more successful programs were compiled in a recent book, Amazon Hacks (O’Reilly, 2003). Author Paul Bausch says his favorite program involves an Amazon feature known as the “wish list.” As you browse Amazon, you can add books to your personal list, a way of not forgetting them. The hack in question makes the wish list viewable on your cellphone. “If I’m in a Borders bookstore, I can answer the question, what was that book I wanted?” Bausch says.
Because of its pervasiveness, the Web is a subject of intensive research, so, of course, there’s a next stage after Web services. It’s called the semantic Web [see “Weaving a Web of Ideas,” IEEE Spectrum, September 2002, pp. 6569]. Although Web services allow a machine to publish its data, making it available to another machine, the two have to agree on the structure of the data they are publishing. In the semantic Web, this sort of agreement will be largely unnecessary.
For example, an airline and a commuter railroad can publish their respective timetables, but if they don’t agree on how timetables are described, programmers who want to develop, say, an automated travel agent application have to manually set up a translation from one to the other. The semantic Web is an attempt to create frameworks that allow the airline and the railroad not just to publish their data but also to offer information about the structure of their data, so that the process of translation can be automated.
But most of the semantic Web’s benefits won’t be seen for some time; Web services are here today. The Web is a ubiquitous, almost transcendent phenomenon, yet its future has just begun to be tapped. It will connect almost every island of data, software, and device on the planet. The conceptual work has been done; it’s time now for the heavy lifting of application development. It’s time for the second Web.
There are many good sites for learning more about SOAP. One of the best is SOAP News, maintained by Dave Winer, who was instrumental in developing the protocol.
The World Wide Web Consortium maintains the XML standard and several others related to Web services. See http://www.w3.org/2002/ws/.
Inside the neural networks that power today’s AI
David Schneider is a senior editor at IEEE Spectrum. His beat focuses on computing, and he contributes frequently to Spectrum’s Hands On column. He holds a bachelor’s degree in geology from Yale, a master’s in engineering from UC Berkeley, and a doctorate in geology from Columbia.
Eliza Strickland is a senior editor at IEEE Spectrum, where she covers AI, biomedical engineering, and other topics. She holds a master’s degree in journalism from Columbia University.
Today’s boom in AI is centered around a technique called deep learning, which is powered by artificial neural networks. Here’s a graphical explanation of how these neural networks are structured and trained.
Each neuron in an artificial neural network sums its inputs and applies an activation function to determine its output. This architecture was inspired by what goes on in the brain, where neurons transmit signals between one another via synapses.
Here’s the structure of a hypothetical feed-forward deep neural network (“deep” because it contains multiple hidden layers). This example shows a network that interprets images of hand-written digits and classifies them as one of the 10 possible numerals.
The input layer contains many neurons, each of which has an activation set to the gray-scale value of one pixel in the image. These input neurons are connected to neurons in the next layer, passing on their activation levels after they have been multiplied by a certain value, called a weight. Each neuron in the second layer sums its many inputs and applies an activation function to determine its output, which is fed forward in the same manner.
This kind of neural network is trained by calculating the difference between the actual output and the desired output. The mathematical optimization problem here has as many dimensions as there are adjustable parameters in the network—primarily the weights of the connections between neurons, which can be positive [blue lines] or negative [red lines].
Training the network is essentially finding a minimum of this multidimensional “loss” or “cost” function. It’s done iteratively over many training runs, incrementally changing the network’s state. In practice, that entails making many small adjustments to the network’s weights based on the outputs that are computed for a random set of input examples, each time starting with the weights that control the output layer and moving backward through the network. (Only the connections to a single neuron in each layer are shown here, for simplicity.) This backpropagation process is repeated over many random sets of training examples until the loss function is minimized, and the network then provides the best results it can for any new input.
When presented with a handwritten “3” at the input, the output neurons of an untrained network will have random activations. The desire is for the output neuron associated with 3 to have high activation [dark shading] and other output neurons to have low activations [light shading]. So the activation of the neuron associated with 3, for example, must be increased [purple arrow].
To do that, the weights of the connections from the neurons in the second hidden layer to the output neuron for the digit “3” should be made more positive [black arrows], with the size of the change being proportional to the activation of the connected
A similar process is then performed for the neurons in the second hidden layer. For example, to make the network more accurate, the top neuron in this layer may need to have its activation reduced [green arrow]. The network can be pushed in that direction by adjusting the weights of its connections with the first hidden layer [black arrows].
The process is then repeated for the first hidden layer. For example, the first neuron in this layer may need to have its activation increased [orange arrow].
READ NEXT: 7 Revealing Ways AIs Fail
Or see the full report for more articles on the future of AI.
Human programmers can actually become more powerful and efficient with Codex
Thomas Smith is a technology journalist and CEO of Gado Images, an AI startup. You can read more of his writing on Codex and other tech topics at DIY Life Tech.
This summer, the artificial intelligence company OpenAI released Codex, a new system that automatically writes software code using only simple prompts written in plain language. Codex is based on GPT-3, a revolutionary deep learning platform that OpenAI trained on nearly all publicly available written text on the Internet through 2019.
As an early Beta tester, I’ve had extensive opportunities to put both GPT-3 and Codex through their paces. The most frequent question I’m asked about Codex is “Will this replace human programmers?” With world powers like the United States investing billions into training new software developers, it’s natural to worry that all the effort and money could be for naught.
If you’re a software developer yourself—or your company has spent tons of money hiring them—you can breathe easy. Codex won’t replace human developers any time soon, though it may make them far more powerful, efficient, and focused.
Why isn’t Codex an existential threat to human developers? Years ago, I worked with a high-level (and highly compensated) data scientist and software developer from a major American consulting firm on a government database project. Our task was to understand how a state agency was using its database to assign grants to organizations, and then to advise the agency on how to improve the database.
When I first started working with my developer colleagues, I had a lot of preconceived ideas about how he’d spend his time. I imagined he’d be hunched over his laptop all day, tapping out code in R or cooking up brilliant formulas in Mathematica to help us better understand our client’s database. I pictured Beautiful Mind-style frantic scribbling on windows, regression analyses, and lots of time spent in front of a screen, writing thousands of lines of Python code.
Instead, my colleague started the engagement by sitting down with the client and spending several days understanding their grant-making process. This led to meetings with individual staff members, stakeholders, the agency’s constituents, and more. Only after several months of this kind of work did he finally sit down to analyze the agency’s data, using R and various graphing libraries. The actual coding and analysis took all of two days. The results of his analysis were spot on, and his program worked perfectly. The client was thrilled.
He later explained to me that actually writing code and running analyses occupies about 1 percent of his time. The remainder is spent working with clients to understand their problems, determining the right software and mathematical models to use, gathering and cleaning the actual data, and presenting results. In most cases, the coding and math itself is a tiny, almost rote, part of the software development process.
This is typical of developers. According to Tech Republic, writing actual code often occupies less than half of a typical software developer’s time, and in many cases, as little as 20 percent of it. That means that even if systems like Codex worked perfectly, they would replace at most half of the job of a typical human software developer, and often less than a quarter of it. Unless someone trains Codex to sit down with clients, win their trust, understand their problems, and break those problems down into solvable, component parts—in short, to do what my colleague did during our project-the system won’t threaten skilled human developers any time soon.
The day when a non-coder can sit down with Codex, write up a spec sheet, and crank out a working piece of software is still far away.
In their paper announcing Codex, OpenAI’s scientists acknowledge this. In their words, “engineers don’t spend their full day writing code.” Instead, they spend much of their time on tasks like “conferring with colleagues, writing design specifications, and upgrading existing software stacks.” Codex’s creators suspect the system may “somewhat reduce the overall cost of producing software” by letting developers “write good code faster. But they doubt it will steal jobs. If anything, they suggest that automating the grunt work associated with software development will open up the field to a broader range of people. It might create a new specialty, too: “prompt engineering,” the often-complex process of crafting the textual prompts which allow AI systems like Codex to work their magic.
Others aren’t so sure. As journalist Steven Levy points out in Wired, Codex may not steal work from individual software developers. But if it makes each developer far more efficient, companies may decide they can get by with fewer of them. Where a project may have required ten developers before, it may only require eight if those developers are assisted by Codex or a similar AI system, resulting in a net loss of two jobs.
That might be true one day, but that day won’t arrive any time soon. Given that demand for developers grew 25 percent worldwide in 2020 despite the pandemic, the real threat to jobs from systems like Codex seems minimal, at least for now. If anything, allowing top companies to get by with fewer developers might make those developers available to mid-tier companies or startups, leading to better software at all levels of the tech ecosystem. Currently, startups often struggle to attract talented developers. If the Googles and Facebooks of the world poached poached fewer top developers, more top-notch talent might be available for emerging, innovative companies.
It’s also important to remember that all of this is predicated on the idea that Codex or systems like it can write code as well as a human software developer. At the moment, it absolutely cannot. OpenAI acknowledges that at launch, Codex’s code contains errors or simply doesn’t work 63 percent of the time. Even writing perfect code 37 percent of the time is a big deal for a machine. But the day when a non-coder can sit down with Codex, write up a spec sheet, and crank out a working piece of software is still far away.
Systems like Codex could create “centaurs,” hybrids of humans and AIs working together to do something faster and better than either could do alone.
That’s why many in the tech community see Codex less as a generator of new code, and more as a powerful tool to assist humans. When I asked futurist Daniel Jeffries whether Codex would replace human software developers, he responded “No chance.” In his words, “It will likely take years before we have a code engine that can generate consistently good routine code and inventive new code.”
Instead, Jeffries imagines systems like Codex creating “centaurs,” hybrids of “humans and AIs working together to do something faster and better than either could do alone.” Centaurs have already proven their value in games like chess, where human/machine centaurs consistently best both human grandmasters and unassisted computers. A human/AI centaur could likely work faster than a human software developer, but would be far more accurate and better attuned to real-world problems than a system like Codex laboring alone.
Popular code repository Github made a splash when it launched Copilot, a code assistance platform powered by Codex. Copilot works like autocorrect on steroids, offering code to complete whole functions or auto-filling repetitive code as a developer types. If centaurs really are the future of artificial intelligence, though, then the system’s name is misleading. In aviation, a copilot is a fully qualified pilot who can take over control of an airplane from the captain if needed. An autopilot, on the other hand, can fly the plane automatically in certain contexts (like when cruising straight and level) but must hand over control to a human pilot when things get dicey (like when landing in bad weather).
GitHub’s Copilot is really more like an autopilot than a true copilot. It can code on its own when tasks are easy and repetitive, but as soon as they get more complex, it requires human intervention. “As the developer”, Github says on its page about Copilot, “you are always in charge.” Ultimately, that’s not a criticism of Copilot or Codex. In aviation, autopilots are incredibly useful systems.
On a given commercial flight, a plane might be on autopilot up to 90 percent of the time. But crucially, human pilots are always supervising the system. And without their 10 percent contribution, planes would frequently crash. Pilots, in other words, are already skilled, safe, and effective centaurs. They could provide a helpful blueprint for similar centaurs in the software development world. That’s probably why GitHub and OpenAI decided to use an aviation metaphor to describe their system in the first place.
Unless Codex improves dramatically in the next few years, human software developers’ jobs are safe. But given the potential efficiency gains, companies and individual developers should begin to explore centaur technologies today. If you’re a developer, brush up on skills like prompt engineering, and apply for access to systems like Copilot and Codex so you can get early experience working with them. If you lead a technology company, start thinking about how embracing centaurs could make your own software development workflows more efficient. And if you teach computer science or coding, start educating your students about AI systems and hybrid centaur approaches today, so that they’re prepared to work with platforms like Codex or Copilot when they enter the job market.
Systems like Codex may fail when they’re pitted against a skilled human developer. But as Codex and its ilk improve, humans who transform themselves into centaurs by combining their skills with advanced AI are likely to become a powerful—and perhaps unstoppable—technological force.
Systems Engineers face a major dilemma: More than 50% of project defects are caused by poorly written requirements. It’s important to identify problematic language early on, before it develops into late-stage rework, cost-overruns, and recalls. Learn how to identify risks, errors and ambiguities in requirements before they cripple your project.