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Are artificial intelligence (A.I.) and deep learning… overhyped?
It wouldn’t be the first time a “hot” technology segment failed to prove as revolutionary as folks initially suspected. Remember 3D printing? The hype suggested that, in a few short years, everyone would be printing replacement parts and household goods; but only a handful of enthusiasts ended up embracing 3D printers as home essentials. There’s also the case of Google Glass, which failed to revolutionize wearable devices—despite Google pumping millions of dollars into the project.
But A.I. is supposed to be different. For starters, the potential upside is nothing less than a wholesale change in civilization: deep-learning algorithms could automate all sorts of processes, from online customer service to medical imaging. Some of this technology is already present, if only in beta: for example, Google’s A.I.-powered microscope for processing and analyzing biological samples for cancer.
Yet those advances haven’t stopped some pundits from suggesting an A.I. “winter” will hit at some point. In a recent blog posting, Filip Piekniewski, an A.I. researcher, counted off instances in which the artificial intelligence hype has greatly exceeded the reality, including a lack of progress in Google’s Deepmind, deep learning (“does not scale,” he concluded), and autonomous driving, which has suffered some high-profile accidents in the past few months.
“Predicting the A.I. winter is like predicting a stock market crash—impossible to tell precisely when it happens, but almost certain that it will at some point,” Piekniewski wrote. “In my opinion there are such signs visible already of a huge decline in deep learning (and probably in A.I. in general as this term has been abused ad nauseam by corporate propaganda), visible in plain sight, yet hidden from the majority by the increasingly intense narrative.”
Until that winter comes, though, A.I.-related jobs continue to pay quite a bit, thanks to the field’s high degree of specialization—there are only so many tech pros with the required knowledge. In an April report, analyst firm McKinsey & Company suggested that various A.I. disciplines could generate “between $3.5 trillion and $5.8 trillion in value annually across nine business functions in 19 industries.” However, it estimated a mere 10,000 people with the right skill sets to meet that rising demand.
So even if artificial intelligence doesn’t deliver on its biggest promises in the short term, chances are good that companies will still need tech pros skilled in deep learning and A.I., if only to figure out how to use this technology as part of long-term strategy.
The post Will an “A.I. Winter” Actually Impact Artificial Intelligence Jobs? appeared first on Dice Insights.
According to a new survey, tech’s reliance on H-1B visa workers might boil down to the attitudes of those in Silicon Valley.
The Mercury News queried Bay Area residents about the visa, and the results are pretty striking: some 71 percent of those queried think the number of H-1B visas doled out annually should be kept the same or increased, while 7 percent had no opinion. (Of those 71 percent, 28 percent think the number should actually be increased.)
That means a mere 22 percent felt the quantity of foreign worker visas should be decreased. Some 38 percent say H-1B workers provide “critical skills” that companies cannot find in domestic employees. And 44 percent say H-1B workers fill skills gaps caused by a “shortage of qualified workers” stateside.
Some 23 percent think H-1B workers “take jobs that would otherwise be filled by qualified American workers.” Meanwhile, 21 percent of tech workers (defined by the survey as “current or former tech employees or those with relatives in tech”) thought that H-1B visas took jobs from qualified American workers.
Meanwhile, the federal government has taken incremental steps to adjust the H-1B program, despite President Trump’s campaign-trail promises of massive reform. For example, the White House has attempted to limit H-4 visas, which go to the spouses of H-1B visa holders; this limits those spouses’ ability to find gainful employment stateside. In addition, a new H-1B visa rule says employees must fill a specialty niche.
Despite these maneuvers, however, the overall number of H-1B visas filed and accepted has gone up during Trump’s time in office. For every 100 workers, Silicon Valley has 2.2 H-1B visa holders. Washington D.C. (and surrounding areas such as Arlington, Virginia) have 2 per 100 employees, suggesting that demand for the visa also remains high in other tech hubs.
The Mercury News poll shows 78 percent feel H-1B visa holders contribute positively to the Bay Area economy; the same percentage think documented immigrants contribute positively to the local economy. The percentage of those who feel positively about immigration only drops when residents were queried about undocumented immigrants (51 percent believe they have a positive influence on the economy).
The post Silicon Valley Has Upbeat View on H-1B Visas: Survey appeared first on Dice Insights.
Project teams are melting pots of professionals with varying opinions, personalities, working styles, and backgrounds—making conflict inevitable.
Not all conflict is bad, however. Technical or issue-focused conflicts can lead to collaborative, outside-the-box solutions if handled properly, noted Dr. Vittal Anantatmula PMP, a former project manager (PM) and professor for the College of Business at Western Carolina University.
Since both PMs and team members play an instrumental role in creating and resolving conflicts, here are four types of clashes that typically occur, as well as some techniques for turning them into positive outcomes.
As long as team members are being respectful and making progress, they should be encouraged to debate the pros and cons of various technical approaches, especially if it leads to a better solution, noted Jennifer Bridges, PMP, founder of education and training site PDUs2GO.
“You can’t allow yourself to get run over, however,” she cautioned. “The project leader should act like a Sherpa and let team members continue long enough to get all the issues and ideas on the table.”
Sometimes people just don’t like each other, or their egos clash. Since personality conflicts can lead to the dismissal of other opinions and the creation of hostile work environments, PMs need to be good at reading emotions and putting the kibosh on personal conflicts.
PMs need to set ground rules from the outset, Anantatmula advised. For example, problems or issues should be stated objectively, without assigning blame or mentioning names. Tech pros may need coaching or training to ensure that they raise issues effectively.
“Having team members share their personality styles can help avoid interpersonal conflicts,” Bridges explained. “The information lets you understand a professional’s frame of reference and when injecting their perspective into a discussion might be beneficial.”
Whether you like it or not, every project team needs a “Dr. Disaster,” she says. That’s her nickname for a team member who makes a habit of predicting when a proposed solution might hit a snag down the road. Don’t invite analytics into brainstorming or “controlled chaos” sessions, since that focus on detail may limit idea generation and creativity.
Using the Agile methodology can help project teams respond quickly to change; however, it can potentially result in scope creep and poorly defined roles, making it difficult for contributors to prioritize one set of tasks over another. Worse, team members may develop different interpretations of the project’s objectives or the quality of deliverables, which becomes fuel for conflict.
When task-related conflicts occur, contributors should ask for clarification, and PMs may need to go back to the basics by reviewing risks, issues or change requests with the project sponsor. The key is to recognize conflict early and apply the right resolution technique.
Encouraging open communications and increasing trust between project team members (and stakeholders) can eliminate task-related conflicts, which typically need to be resolved by negotiating a solution that is either completely or partially satisfactory to all parties. If that doesn’t work, the PM may need to use formal authority to resolve the conflict.
“Nothing goes as planned,” Bridges warned. “Keep project team conflicts from becoming personal by getting to the root of the problem fast and focusing everyone on the deliverables.”
Unequal Commitment and Involvement of Team Members
Successful team performance hinges on the commitment of individual members. Conflicts can arise when team members perceive that they are contributing more than others, or that workloads or resources are uneven. The ensuing debates often digress into a blame game.
Such arguments need to be recognized and treated as people-focused conflicts, Anantatmula noted. In addition to increasing transparency and communication, and encouraging team building, issuing team contracts or agreements at the beginning of a project can help increase involvement and commitment levels.
“PMs need to be directors during the initial stages of a project,” he said. “Once the groundwork is laid and things are going smoothly, their job changes from director to facilitator.”
The post Project Manager Tips for Dealing with Conflict in Project Teams appeared first on Dice Insights.
Some 70 percent of companies claim they’re using a form of artificial intelligence (A.I.), according to a new report by Constellation Research. That includes machine learning, deep learning, natural language processing, and cognitive computing.
But while companies are interested in what A.I. can potentially do for them, many aren’t willing to invest massive amounts of money in the endeavor. Some 92 percent of respondents reported overall A.I. budgets of less than $5 million, with 52 percent paying less than $1 million. However, most plan to increase their A.I.-related spending over the next year.
For companies pursuing an artificial intelligence strategy, Constellation recommends starting as early as possible, as building out platforms and training algorithms takes time. A company’s data scientists and executives must also start thinking about how to structure their existing databases for interaction with the eventual A.I. platform.
“Conduct a workforce audit and assess your A.I.-talent needs,” the report added. “Create a staffing plan that takes into account your internal talent supply, ability to reskill, recruiting needs, partnerships (with industry leaders or universities) and poaching by competitors.”
Constellation Research offers a number of other tips for gearing up for A.I., so check out the report if you’re interested. The firm interviewed 50 C-level respondents (i.e., CEOs, CIOs, CTOs, CDOs) for the survey portion.
For tech pros, this kind of data is good news. If businesses are willing to spend more on A.I. initiatives in the short term, that means more jobs and projects to work on. Earlier this year, analyst firm McKinsey & Company published a report suggesting that fewer than 10,000 professionals actually have the necessary skills to tackle the biggest A.I. problems, which is a key reason why A.I.-related salaries have climbed into the stratosphere. That report also suggested that A.I.-enhanced industries constitute a multi-trillion-dollar market, placing a high ceiling on the demand for talent.
Developing into enough of an A.I. expert to pull down a million-dollar salary takes years and years of education and training; but those totally new to the artificial intelligence field can nonetheless pick up some basic knowledge and skills pretty rapidly, thanks to companies and educational organizations rolling out a variety of learning tools.
For example, Google offers a three-hour course on deep learning and machine-learning tools via its Google Cloud Platform Website, and Facebook hosts a series of videos that break down fundamental A.I. concepts such as algorithms. In terms of online learning, there are Coursera and Udacity, which offer instruction in A.I., and some self-teaching opportunities via materials on GitHub.
The post Artificial Intelligence Has Companies’ Interest, But Not Their Cash appeared first on Dice Insights.
For some reason, a segment of the iOS developer community would love to have Xcode (the IDE for macOS, iOS, and other Apple platforms) on the iPad. One enterprising soul created a proof of concept for it, and – it’s something.
Software developer Louis D’hauwe tasked himself with creating Xcode for iPad as a side project. It’s got some legs, too. The app supports side-by-side and windowed views for iPad, and has a familiar file tree structure in a sidebar. The app launch screen is a list of projects you’re working on.
But that might be where the magic ends. As D’hauwe notes, code created in this Xcode for iPad concept is compiled on a Mac, “which acts as a remote server.” He says it “generates an .ipa that is uploaded by the Mac,” which is then installed on an iPad.
I created a proof of concept Xcode for iPad! Code is compiled on a Mac, which acts as a remote server. Generates an .ipa that is uploaded by the Mac, the iPad then installs it. pic.twitter.com/H7wO1RtAXn
— Louis D’hauwe (@LouisDhauwe) June 16, 2018
In a subsequent tweet, D’hauwe says the iPad Xcode app will let developers write code on a mobile device and compile directly to a Mac server. In his view, there’s suddenly no need for a Mac.
But the project is also patched together. It uses D’hauwe’s Savanna mobile IDE (built for the Cub programming language) and regexs for syntax highlighting. It also requires raw XML editing; there’s no storyboard support.
There’s a lot to unpack with this. It’s a very intriguing tool. It’s also not one that most developers would want as an end-to-end app creation tool. Storyboards work just fine in this Xcode for iPad project, but there’s no visual editor. That’s great for making minor adjustments, but creating in a proper design environment is much faster (and probably yields better results).
But as a means to pick at an Xcode project on the fly, this might be just what we need. If you’re the type to bring an iPad on vacation instead of a Mac, Xcode for iPad means those moments of inspiration could yield instant results instead of notes.
Even so, for many professionals, there’s just no replacing the Mac. It’s far more powerful, has a proper keyboard, and there’s access to other professional apps (such as Sketch).
The post This Xcode for iPad App May Make You Appreciate the Mac appeared first on Dice Insights.
Omitting key information from résumés and job applications can have serious consequences for job seekers. But for departing employees with one foot out the door (and the resignation already submitted), there’s no point in brutal honesty, especially in the context of an exit interview.
The stark truth is that companies rarely act on suggestions or complaints made by workers who are leaving. “Don’t be a hero on the way out,” advised Robbie Abed, a former IT consultant, founder and author of “Fire Me I Beg You.”
If you really want to make a difference, he added, “be a hero while you’re there.”
With that in mind, here are three things you should (and shouldn’t) say during exit interviews.
When to Skip the Interview
If you work for a large company with thousands of employees, few people will likely even notice if you politely opt out of an exit interview; this is especially true at those firms that tend to schedule such interviews at the absolute last minute, as a pure formality.
It’s a different matter at smaller and midsize firms, where you should agree to the exit interview process so you don’t burn any bridges.
“If you really want to have a productive conversation, invite your former boss to coffee or lunch in a month or two [after you leave],” Abed suggested. “He’s far more likely to be open and candid with you about his decisions and authority after you leave the organization.”
Take the High Road
No matter how bad things were or how poorly you were treated, use your interview to focus on the positives, such as the rewarding aspects of your job. “The interviewer is sure to ask why you’re leaving and how the company and your manager could improve,” explained Erik Dietrich, a former IT consultant and founder of DaedTech LLC.
Since you have everything to lose and nothing to gain at this point, pivoting to the career benefits of your new opportunity is a more effective strategy, he added. Focusing on the future can keep you from sounding like a bitter complainer or going on a negative rant. Highlighting the things that attracted you to a new position indirectly communicates why you were dissatisfied with your old job.
For instance, you can explain that it wasn’t an easy decision, but that you received a new opportunity that was too good to pass up.
If you feel compelled to offer criticism, throw a couple of softballs out there. For instance, mention that the 401(k) match could have been higher, or that you prefer a culture that invites bold risk-taking.
“Stick to broad cultural things when offering suggestions for improvement,” Dietrich said. “Otherwise, the interviewer may get defensive or respond by making a counter-offer, leaving you in an awkward position.”
Remember: The Exit Interview is On-Record
No matter what you’re told, any negative feedback you provide to HR will likely make its way back to your boss and colleagues. Some companies may even ask you to sign off on the interviewer’s notes. And since you may be unaware of the interviewer’s hidden agenda, don’t put anything in writing.
Recognize that it’s a very small world out there, Abed noted: “Don’t express frustration or mention names… You never know when you might cross paths with a former teammate or boss again.”
And if you’re planning to file a lawsuit or claim against the company, it’s possible that things said in an exit interview might impact your case. You certainly don’t want to repeat office gossip or accuse your boss or co-workers of inappropriate behaviors (which could be construed as slander).
It’s definitely best to keep the exit interview professional and productive. After all, you’re already voting with your feet.
The post Your Exit Interview: Things You Should (and Shouldn’t) Say appeared first on Dice Insights.
In the June update of the TIOBE Index, which attempts to rank the world’s programming languages by popularity, not much has shifted: Java, C, C++, Python, and C# remain the most “popular,” while smaller languages fight to climb the lower ranks. TypeScript has broken into the Index’s top 100 languages (arriving in 93rd place), but that’s the only thing the firm saw fit to call out in its note accompanying the data.
In May, TIOBE announced a snafu in its methodology. In order to create its rankings, the firm leverages data from a number of sources, including Google, Wikipedia, YouTube, and Amazon. But Google recently tweaked its search algorithms, scrambling TIOBE’s list. “We see a lot of… huge ups and downs. This has to do with Google re-indexing, which is quite volatile,” TIOBE stated at the time. “For this we are going to implement compensation functions for the TIOBE Index in the near future because we can’t accept that Google hits are only half of the number in comparison to the previous month.”
For the June update, TIOBE apparently added a “smoothing function” in order to avoid any “strange spikes.” Nonetheless, there’s one oddity: Objective-C has climbed from 18th place to 12th place over the past year, while its successor, Swift, has dropped from 12th place to 15th. This is strange, given how much effort Apple has poured into getting developers to switch from Objective-C to Swift.
Ultimately, though, TIOBE is a gauge of programming-language popularity, and as such, the languages in its top ranks rarely (if ever) shift. For developers and other tech pros, these rankings are a monthly reminder that, no matter how “cool” a new language or platform might seem, it’s well-established languages such as Java and Python that continue to drive much of the tech world’s progress.
The post TIOBE’s Programming Language Rankings Show Little Change in June appeared first on Dice Insights.
Developer interest in Kotlin spiked when Google named it an official Android programming language. Released in 2011 by JetBrains, and continually fine-tuned, Kotlin is now up to version 1.2.41, with boosted interoperability with Java, the “original” Android development language.
But the rise of Kotlin doesn’t mean Android developers can (or should) abandon Java entirely. Kotlin leverages Java libraries, provides Java APIs, and integrates with Java frameworks; if you know one language, you should learn the other. During a Kotlin developer interview, you’ll inevitably be asked questions about both.
When interviewing candidates for a Kotlin-related role, Rimantas Benetis, technology director at Devbridge Group (a Chicago-based custom software, web and mobile app development company), looks for a solid understanding of Kotlin, as well as a straightforward approach to problem-solving.
James Baca, a senior Android developer at Bamtech Media, a technology services and video streaming company headquartered in New York City, advises avoiding facile answers by digging deep and illustrating your agility with Kotlin.
With that in mind, here are a few sample Kotlin developer interview questions:
“What advantages do you think Kotlin has over Java, and why?”
What most people say:
Most people pick some design decisions that they feel are better, such as Kotlin’s nullability declaration requirements, and leave it at that.
What you should say:
“Kotlin has a huge advantage of not being constrained to design decisions that were made 23 years ago. Kotlin’s developers looked at what made Java difficult to use, examined what made newer languages more pleasant to work with, and put [those elements] together to complement one another in Kotlin.”
Baca also suggested candidates give a few examples of how Kotlin is potentially more effective than Java, such as allowing late initialized variables (on platforms such as Android, you don’t always have the variable contents at declaration time).
Why you should say it:
It avoids answers that most other candidates would say, and shows that you understand the reasons why Kotlin exists in the first place.
“How do you declare variables in Kotlin and how does it differ from its Java counterpart?”
What most people say:
Most Kotlin developer interview candidates answer that the syntax is different between Java and Kotlin. In Java, you declare the type first, followed by the variable name; Kotlin does this in reverse and uses a colon.
What you should say:
“Besides the obvious syntax differences, Kotlin has two types of variables. They can be declared as read-only, using the ‘val’ keyword, or mutable using the ‘var’ keyword. Java doesn’t have read-only variables. Instead, you have to make the variable private and add a public getter to enforce read-only. Also, and arguably most important, is that you have to decide if the variable can contain nulls in Kotlin.”
Why you should say it:
This answer shows that you understand how to use the data encapsulation features of Kotlin, which indicates that you understand the importance of one of the four object-oriented programming principles.
“It also shows that you know you need to decide if a variable should be null,” Baca noted. “And, by discussing declarative null syntax, you demonstrate that you are implicitly aware of ‘The Billion Dollar Mistake,’ more commonly referred to as NPE or Null Pointer Exception, and how to avoid it.”
“What is the difference between ‘const’ and ‘val’?”
What most people say:
“There is no difference. ‘Const’ and ‘val’ are the same.”
What you should say:
“’Const’ is a compile time constant that never changes. ‘Val,’ on the other hand, is a variable with a caveat, which is that it’s a read-only variable. It’s possible that its value will change. However, it doesn’t have a setter for directly changing the value.”
Baca also recommended giving an example: “In the sample below, see that ‘isEmpty’ is read-only, but its value can change.”
val isEmpty: Boolean
get() = this.size == 0
Why you should say it:
The answer shows you know how to take advantage of the simple-yet-powerful language features in Kotlin.
“Explain the data classes used in Kotlin and why they are useful.”
What most people say:
“It’s a time-saver. They are just plain Java objects that have equals() predefined so that you don’t have to write boilerplate equals code for every new class.”
What you should say:
“Data classes have several benefits. They auto-generate equals()/hashCode() functions, a very readable toString() output, and componentN() functions to take advantage of destructuring declarations, as well as copy method for generating copies of the class.”
Why you should say it:
“While the first answer is sort of acceptable, it doesn’t show all of the time-saving features,” Baca said. Keep that in mind during your next Kotlin developer interview.
The post Kotlin Developer Interview Questions: How to Respond appeared first on Dice Insights.
Back in 1981, I graduated from university and got my first programming job. That year a program was released that would supposedly bring about the end of human programming. Called The Last One, it failed despite a lot of hype. But will A.I. succeed where that early attempt crashed and burned?
A few weeks ago, Rice University researchers released an artificial intelligence application named Bayou that supposedly can help you write code. Partially funded by Google and the U.S. Department of Defense’s Defense Advanced Research Projects Agency (DARPA), Bayou synthesizes an application using Java code extracted from hundreds of thousands of open-source projects. Then comes the clever bit: the A.I. matches the code specification, which is a simple search phrase, and returns the most likely code equivalent (along with some lower-scoring alternatives).
Is Bayou trying to follow in the footsteps of The Last One, promising to liberate humans from the tyranny of coding their own software? In reality, this new platform just assists you in programming with Java. It’s certainly not going to put you out of a job; in fact, it might make you more productive.
The hype comes from Bayou being A.I.-driven, which sparks the usual fears about A.I. replacing human jobs. But will artificial intelligence eventually take over coding? Let’s pick apart what programming actually is, along with the current capabilities of artificial intelligence.
Let’s Actually Define ‘Artificial Intelligence’
Most people unaware of the current state of A.I. consider it to be AGI (Artificial General Intelligence), also known as “strong A.I.” AGI is that theoretical point when an A.I. platform can think and reason like a human being; you’ve seen some variation of this in lots of sci-fi movies over the years. Of course, current A.I. can’t walk into a house and brew coffee, much less make the intuitive leaps that mark human cognition. While I have zero doubt that AGI may emerge someday, I’m not losing any sleep waiting for the Singularity.
The A.I. that we see in real life is ANI (Artificial Narrow Intelligence) or “weak A.I.” This encompasses things such as categorizing images on Google, reading license plates, and powering the routines of Alexa and Siri. Boiling it down even further, ANI can be trained to use highly sophisticated methods of classifying data and recognizing patterns, but it can’t “think” like a human brain.
Complexities of Programming
Back in 1981, the whole programing scene was a lot simpler: there were just mainframes, minicomputers, the BASIC-powered CBM-PET, the Apple II, the Tandy TRS-80 and some other PCs. Now you have a diverse mix of computing devices (PCs, mobile, etc.) linked via the internet to a worldwide network of servers. A multiplicity of programming languages and diverse legacy code bases just makes everything that much more complicated.
Software developers have to worry about user interfaces, graphics, audio, relational or NoSQL databases, the efficiency of algorithms, and maintaining existing software. In other words, you’d need a pretty strong A.I. to replace a human programmer, who needs to spend every day thinking of creative solutions to nuanced problems.
Fortunately, weak A.I. can assist programmers in their daily tasks (Note: I’m not talking about toolkits that programmers can use to build A.I. platforms, but specifically A.I. tools to assist programmers). Let’s break down a few:
Writing tests is one of the most tedious bits of programming, so it makes sense to automate it if you can. The Oxford company Diffblue, founded by two Oxford University computer science professors, has developed a system for doing just that. This platform reads your source code, analyzes it, reads any tests, and then generates tests to fill in the gaps. It can also suggests potential improvements from refactoring.
Diffblue is available only for Java (at least for the moment), although the creators are currently recruiting C++ developers and have raised $22 million in “Series A” funding.
Ubisoft’s Commit Assistant A.I.
You can expect video-game companies to use A.I. in their games for controlling virtual baddies, but Ubisoft has gone a step further with its Commit Assistant AI. When a developer commits code to their version-control system, the A.I. examines the code and, according to Ubisoft’s figures, catches 60 percent of bugs with 30 percent false positives, saving developers about 20 percent of their time. This was developed in conjunction with the Canadian University of Concordia, which was given access to ten years of Ubisoft’s code (presumably C++) to train the A.I.
Later this month, at the Mining Software Research conference in Gothenburg, Sweden, the research behind Commit Assistant will be made public.
At this year’s Build developer conference, Microsoft announced IntelliCode for Visual Studio and C#. It’s a step up from IntelliSense, which provides an alphabetical list of available matching functions, as well as variables as you type.
IntelliCode is A.I.-powered, and reduces the amount of typing to complete a line. Typically, some functions can have three, four or even 20 or so different overloads—these have the same function name, but with different types and numbers of parameters. IntelliSense just shows the list of overloads that you can scroll through; IntelliCode suggests the most useful overload.
IntelliCode can do this because the A.I. has been trained on over 2,000 Github repos (each with over 100 stars). It’s still early days, but expect a lot more from this platform.
Last year, Microsoft created an A.I. named DeepCoder (PDF) that, much like Bayou, synthesized programs from existing code bases to solve programming challenges. This was simpler in its functionality than Bayou, with a base of 500 programs solving relatively straightforward programming challenges. It leverages methodologies such as neural nets.
(It’s hard not to imagine that Google and Apple are working on the same kind of capability for XCode or Android Studio. What developer wouldn’t want an A.I. to locate bugs (and fixes) for them?)
Nobody with any knowledge of artificial intelligence expects it to replace programmers, but the A.I.-powered tools that have begun to appear will certainly allow programmers to become more productive. Keep an eye on how these platforms can improve your own workflow.
Google recently published a corporate blog posting breaking down its ethical principles for artificial intelligence (A.I.). “How A.I. is developed and used will have a significant impact on society for many years to come,” Google CEO Sundar Pichai wrote. “As a leader in A.I., we feel a deep responsibility to get this right.”
Those principles are pretty straightforward: Google’s A.I. should be “socially beneficial,” avoid “creating or reinforcing unfair bias,” feature strong safety and security features, incorporate “privacy design principles,” and uphold “high standards of scientific excellence.”
In addition, any Google A.I. platform should be “accountable to people,” meaning that humans should be able to direct and control its workings. And last but certainly not least, the A.I. must “be made available for uses that accord with these principles.” In other words, Google will watch to make sure that its A.I. isn’t easily adaptable to “harmful use,” and that it can scale in a way that has a widespread positive impact.
At the same time, Google has pledged to not pursue technologies that are likely to cause overall harm. It also won’t research A.I.-based weapons. This is clearly a nod to Google’s controversial contract with the Pentagon, which it won’t renew after employees at the company protested. That contract, intended to use Google’s A.I. to interpret objects in images and video feeds, could have been utilized to improve the “eyesight” of military drones, which are often used to fire missiles at targets.
Google’s A.I. ethical framework is considerably more detailed than some of the others pushed forward over the past few years. For example, OpenAI, a nonprofit organization devoted to figuring out how A.I. can most likely benefit humanity as a whole, has been somewhat vague about ethical specifics. From OpenAI’s introductory blog posting:
“We believe AI should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as possible. The outcome of this venture is uncertain and the work is difficult, but we believe the goal and the structure are right. We hope this is what matters most to the best in the field.”
As A.I. grows more sophisticated in coming years, and its impact more widespread, discussions of ethics will increasingly move out of the theoretical realm into the real world. And the ethical conundrums are coming. Militaries will throw lots of money at researchers in an attempt to weaponize A.I.; criminals may attempt to use platforms such as Google Duplex to launch social-engineering attacks on an industrial scale; and there’s always the potential for unintended consequences, such as a “smart grid” deciding to shut down on its own.
Even if other companies join Google in publishing detailed ethical guidelines, that might not be enough to prevent at least some of these negative consequences of A.I. Over the long term, the tech industry as a whole may face some hard decisions over how to shape A.I. in a way that’s truly beneficial.
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