“We wanted flying cars; instead we got 140 characters,” is venture capitalist Peter Thiel’s famous credo.
But though a freeway in the sky seems as fantastical as ever, we are going to get something even better: a self-driving car.
Such a robot, fully aware of its environment, with 360-degree vision and peerless driving skills, is a matter of when, not if. Humans’ fascination with these machines seems limitless, even though autonomous cars could turn us into mere cargo. And unlike airborne cars, self-drivers could prevent the 1.2 million deaths caused by traffic accidents every year.
It’s no utopian fantasy. Among our Top Ten Tech Cars this year are a robotic Audi that tears around racetracks like a professional driver and an electric Tesla whose impressive autopilot skills are as close as the nearest showroom.
Booming sales should also help accelerate the technological pace. Americans parked 17.5 million new cars in their driveways in 2015, more than any year in history, and the Chinese bought even more. That left the industry awash in profits and able to spend heavily on R&D to bring pioneering cars and technologies to market.
So, carbon-based life form, the message is clear. If you enjoy driving, get your fill while you can. In our list of the 10 cars that are rocking our technological world, we’ve included more than a few choices to help maximize your motoring pleasure. After all, we’re only human.
Last week, Tesla—best known for its electric vehicles—announced its latest product: roof tiles with built-in solar cells. To succeed where other companies have failed, engineers say they must strike a delicate balance among cost, aesthetics, safety, and performance.
Widespread adoption would yield clear environmental benefits, of course. Solar power could be a way to lower carbon dioxide emissions and combat global warming, Tesla CEO Elon Musk said in a presentation. Musk sees solar roofs as part of his plan for running the world on clean, sustainable energy.
Yet Ronnen Levinson, a mechanical engineer at Lawrence Berkeley National Lab who studies ways to keep roofs cool, points out that “Tesla isn’t offering a new idea.”
In the past, solar power has had mixed results in the United States. Years ago, the Obama Administration supported the solar power company Solyndra, which went bankrupt in August 2011. Dow Chemical recently tried a solar shingles project that it shut down in July.
Tesla declined to comment for this story. Former Solyndra CEO Brain Harrison and Greg Bergtold, a business advocacy director at Dow Chemical, also declined to comment.
The discernible difference with Tesla’s photovoltaic roofing material, Levinson says, is that the company will offer integrated tiles. Instead of buying a roof, paying for workers to install it, then buying new solar panels and paying for extra labor, you can consolidate the cost–which could be cheaper. Musk said there are about 4 to 5 million new roofs in the United States every year, with more worldwide, so there is a market.
“Over time, every house would become a solar house,” Musk said.
During the presentation, Musk unveiled solar-cell glass tiles that could integrate with Tesla’s new Powerwall 2 battery as well as electric cars. TechCrunch reported that Musk claims the new roofs would last two to three times as long as typical 20-year-cycle roofs and be more impact resistant.
The marketplace challenges this product faces, Levinson says, are a mix of different factors: where you are, how much local electricity costs, whether you are buying a new home and new roof or need to replace a worn-down roof, and what the solar availability is. If you’re away from home during the week and only use electricity on the weekends, then storing electricity might not be practical. Also, not all states and utility companies have measures in place that allow homeowners and businesses to sell their excess solar energy, negating what would otherwise be an additional financial benefit.
Angèle Reinders, an industrial design engineer at the University of Twente in Enschede, Netherlands, works on integrating photovoltaics into infrastructure. She says consumer acceptance might also be difficult to get.
“If it’s affecting their building or their daily behavior, people are usually quite reluctant to adapt to that new technology,” she says.
In terms of performance, she wondered why Tesla doesn’t specify what technology the solar cells use or their tested efficiency—Musk has claimed that they achieve 98 percent of the efficiency of traditional solar panels. “I would like to talk with Elon Musk and ask him personally,” says Reinders. “You can’t put something on the market and not say what sort of technology’s in it,” she says.
Rodrigo Ferrão de Paiva Martins, a materials scientist at the Instituto de Desenvolvimento de Novas Tecnologias in Caparica, Portugal, is working on thin-film solar cells. He says it’s possible to get a high enough energy efficiency for market practicality.
He begs to differ with Musk, saying that solar shingles are inherently less efficient than traditional solar panels. He notes that they can deliver about 3 to 4 percent conversion efficiency. They become cost effective at about the 10-percent-efficiency mark, and there’s a way to get there by tweaking the manufacturing process.
When solar cells are made, they are typically put in a mold and heated, leaving tiny holes in the material. By heating the materials at higher temperatures than normal, these holes could be smoothed out, improving efficiency.
Levinson, however, pointed out that efficiency can be a misleading way to think of the cost effectiveness problem—because it is a product of several variables such as location and the local price of electricity.
There could also be various technical and safety challenges.
Reinders says that glass would not be the most insulating material for cold climates and stressed that there would be a tradeoff between performance and the need to adhere to building code requirements. In the Netherlands, for example, there are rules that spell out what roofing material must be in terms of fire resistance as well as being non-toxic, durable, and resistant to erosion.
Bjørn Petter Jelle of the Norwegian University of Science and Technology and SINTEF Building and Infrastructure in Trondheim–who is investigating resilient solar cells–says it’s important that the new roofs are resistant to harsh weather conditions such as snow, ice, wind, rain, and ultraviolet radiation.
Also, there is a tradeoff between aesthetics and performance. Jelle knows of a man in a city outside of Trondheim who bought curved Chinese solar panels to match the homes of his neighbors, even though they were less efficient than flat panels.
Levinson says it remains to be seen whether the Tesla product will succeed, but Reinders says “the timing is really well done” with respect to market readiness.
There’s at least one buyer. Don Weidner, a Tesla Model S owner and founder of Formidable Ventures, says “I’m blown away by how good they look.”
He says he just moved into a new house in 2015, but assuming Tesla lives up to Musk’s promise regarding the cost for new roofs, “the very next time I move or build or need a roof, I would absolutely” buy one.
When every car on the road is an autonomous car, we won’t have to worry about what kind of driver everyone else is. Before that happens, there’s going to be a very long and messy period where autonomous cars will be sharing the road with human drivers. It’ll be important for autonomous cars to understand and predict what the humans around them are trying to do, which is a very difficult problem, because humans are notoriously irrational: we all have different intentions, goals, preferences, objectives, driving styles, and we may or may not be looking at our cell phones.
At UC Berkeley, researchers have come up with a way for autonomous cars to actively gather information about the human drivers around them. All it takes is a little gentle probing.
Generally, robots gather information about humans passively: they watch what humans do, take notes, and try to use those data to predict what humans will do next. This approach is somewhat limited in its effectiveness, because humans don’t always do the things that would provide the most useful information.
A more active approach would involve trying to find ways to get the humans to take actions that would generate the information that the robots need. That sounds a bit complicated, but it’s something that we human drivers do all the time. For example, if you’re at a four-way stop and it’s technically not your turn to go but you’re not sure if the other driver is paying attention, you might inch forward a bit to see how they react.
At IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) earlier this month, Dorsa Sadigh, S. Shankar Sastry, Sanjit A. Seshia, and Anca Dragan presented a paper on an algorithm that can plan robot actions to gain information about humans. In other words, it gives robots ideas on how to use little nudges to get a better sense of what humans are thinking.
We explored planning for an autonomous vehicle that actively probes a human’s driving style, by braking or nudging in and expecting to cause reactions from the human driver that would be different depending on their style.
Here are some examples of the kind of actions that the algorithm plans for autonomous cars to determine whether the human drivers around them are passive, aggressive, or paying attention:
Scenario 1: Nudging In to Explore on a Highway
The autonomous car actively probes the human by nudging into her lane in order to infer her driving style. An attentive human significantly slows down (timid driver) or speeds up (aggressive driver) to avoid the vehicle, while a distracted driver might not realize the autonomous actions and maintain their velocity, getting closer to the autonomous vehicle.
Scenario 2: Braking to Explore on a Highway
The robot slows down to actively probe the human and find out her driving style. An attentive human would slow down and avoid collisions while a distracted human will have a harder time to keep safe distance between the two cars.
Scenario 3: Nudging In to Explore at an Intersection
In the active condition, the autonomous car nudges into the intersection to probe the driving style of the human. An attentive human would slow down to stay safe at the intersection while a distracted human will not slow down.
Once an autonomous car has collected this data, it can then adjust its behavior to compensate for whatever the humans around it are doing. It’s interesting to think about how data like these could be used beyond just the scenarios in which they get collected. For example, if autonomous cars consistently notice that particular humans drive aggressively, perhaps they could label them as such, and share that information with other autonomous cars, or even with other human drivers. Or insurance agencies. And, you know, maybe suggest that they get counseling.
The results of studies with humans in driving simulators “suggest that robots are indeed able to construct a more accurate belief over the human’s driving style with active exploration than with passive estimation.” The authors readily admit that finding the appropriate line between exploration and exploitation is still a challenge, and that it will be important to find actions to take that are safe. Perhaps the biggest issue, as the authors point out, is that “people might not always react positively to being probed.”
Information Gathering Actions over Human Internal State, by Dorsa Sadigh, S. Shankar Sastry, Sanjit A. Seshia, and Anca Dragan from the University of California at Berkeley, was presented this month at IROS 2016 in Seoul, Korea.
The Grand Cooperative Driving Challenge (GCDC) may take cooperative automation of vehicles to the next level and help speed up implementation. The 2016 edition, which took place 28–29 May, was an innovative and competitive demo event on the A270 highway between Helmond and Eindhoven, in which 10–12 European teams competed with each other. The challenge was a combination of vehicle automation (making it selfdriving) and vehicle-to-vehicle and vehicle-to-infrastructure communication. GCDC 2016 was organized to provide a basis for cooperative, automated driving in an international context.
GCDC 2016 was the second edition. The first GCDC was held in May 2011 in Helmond, The Netherlands. The challenge is open for anyone interested in cooperative driving. Apart from the communication technology itself, it is the application in the vehicles that is key to enabling good maneuverability through automated acceleration, braking, and steering. Three different automated lane-changing scenarios were considered:
Vehicles that merge or join a line of vehicles, a platoon (before changing lanes, the vehicles automatically negotiate how to merge the new vehicle into the line)
Automated crossing and exiting a junction (when entering a T-junction, the vehicles automatically negotiate which vehicle passes first, second, third, and so on)
Automated space-making for emergency vehicles in a traffic jam (this scenario is a demo scenario that was not part of the competition).
Ten teams from Latvia, Spain, France, Germany, Holland, and Sweden took part in the contest. The winners were students from Halmstad University, Sweden, competing with a Volvo S60. Second place was awarded to Team AnnieWay from the Karlsruhe Institute of Technology, Germany, competing with a passenger car from Mercedes. The third-place team was from KTH Royal Institute of Technology, Sweden, competing with a Scania truck. GCDC 2016 was organized within the FP7 project i-Game, with four partners: The Netherlands Organization (TNO) for Applied Scientific Research, Eindhoven University of Technology, IDIADA, and Viktoria Swedish ICT.
Companies are evolving in a fast-changing environment. Organizations are forced to adapt and becoming more flexible. Resources, whether people, materials or finances are essential to project survival and success. But resources are limited. A recent KPMG study showed that a third of failed projects face resource availability problems. The challenge is that of finding a balance between demand and supply.
The quest for greater flexibility
In recent years, project management has changed dramatically. Projects are strategic in nature and companies manage numerous projects simultaneously. The expansion of the geographical framework and new ways of working further complicate human resource management for project delivery.
Human resources and the project
The project manager manages employee availability so that they are available at the right time. It’s akin to putting together a puzzle! In a multi-project context, resource management is even more complex.
Planning
Project leaders plan projects and allocate resources to tasks. The goal is to optimize the use of these resources. Project management software can help provide a global view of resource allocation and availability. The project manager can search for the talents they need in the system. By recording profiles, software can help planning by generating lists of appropriate and available resources for each stage of the project.
Absence management capabilities can enable you to centrally monitor absences, days off and holidays. Updated regularly, good PM software provides a clear picture of the availability of resources and their allocation to the various projects.
Automating project resource management can be done via project management software, which facilitates project planning and aims to optimize the use of resources.
Project tracking and time sheets
Companies that operate by project prefer to analyze and reassess the time if necessary. Time sheets and the integration of day off management, whether they are planned or not, are features that help companies maintain a clear vision of their human resource allocations. All views, charts, and dashboards in project management packages transmit a real-time view and allow adjustments to resource allocations to be made as the project progresses. Project management software indicates potential risks of work overload and unavailability with appropriate notifications. This allows the PM to react quickly and make the required changes.
A question of costs
By maximizing the use of resources, enterprises optimize the results of their greatest asset: employees. Project management software can integrate resource costs and plan the project very precisely in financial terms. In this way, stakeholders can constantly monitor the comparison between the planned budget and the actual budget.
An advantage
With more optimal resource management, creating project teams becomes easier. The teams are then more effective and adaptable. Even if the success of a project doesn’t rely entirely on resource management, it still remains a determining factor. Companies using a resource management tool to simplify project management have an inevitable advantage.
Here are our tips to ensure effective management of project resources:
Centralize resource management for a comprehensive view and transparency within the organization
Optimize the use of resources based on project portfolio priorities
Never separate project planning from your resource capacity
Strengthen project team culture to improve productivity
At a panel today at SXSL in Washington, D.C., Slack CEO Stewart Butterfield, Transmedia Capital Managing Partner Chris Redlitz, EpiBone CEO Nina Tandon, Coalition for Queens founder Jukay Hsu and Jenna Wortham of The New York Times Magazine discussed technology’s role in solving some of the country’s most critical problems.
To some, the U.S. is not the land of equal opportunity, and the so-called American Dream — that everyone can succeed with hard work and determination — is a myth. The existence of systemic racism, sexism, heterosexism, classism, homophobia, transphobia, ableism and anti-semitism makes it so that the American Dream will never come true for so many people. Those discriminatory barriers ensure that only white people have an unencumbered shot at it. So, it’s no wonder why we see that some of the wealthiest tech companies are led by white men.
“Silicon Valley is the engine for wealth creation,” Slack CEO Stewart Butterfield said at South by South Lawn today. “They’ve displaced energy, they’ve displaced financial services and if we don’t start including a broader array of people in that, the same group of people is going to rise to the top.
“I bet there’s a couple we could all agree on,” Butterfield said. “Criminal justice reform would be a big one. Equal opportunities would be another one. Climate change would be another one. Those are ones where technology is more or less applicable.”
SXSL was inspired by President Barack Obama’s visit to SXSW earlier this year. During Obama’s remarks at SXSW, he outlined three ways in which technology can improve our country: making the government work better through tech, tackling big problems in new ways, and using big data, analytics and tech to make it easier to vote.
In alignment with Obama’s previous remarks on tech being used for on-demand food delivery, Wortham noted how technology is producing frivolous products like hoverboards and on-demand laundry services. But while some uses of technology are more admirable than others, Butterfield said he wouldn’t want to live in a world where people weren’t able to spend time making seemingly frivolous or silly innovations.
“While that’s going on, though, I think there is really significant technological changes like we just saw a solar power under three cents per kilowatt hour for the first time and i didn’t think that would happen for another 15 years. And that’s going to have a pretty profound effect on the shape of our world’s countries,” Butterfield said. “So, it’s a tough one. Part of what gives us the good results that we want is that unfettered exploration of all the possibilities and people trying out different things.”
He went on to say that Twitter is a good example of that. At the beginning, people were tweeting about what they had for breakfast.
“On the negative side of humanity and culture, there’s a lot of harassment and hate, but on the other side, Black Lives Matter wouldn’t have happened without Twitter,” Butterfield said. “And the Arab Spring. There’s a lot of political movements its enabled [Twitter is] an amplifier for our best and worst tendencies, but gives us a lot more facility to impact the world.”
In order to really impact the world, the tech industry does need to work with regulators, which means that things might not change as fast as we want them to. So, in areas like education, health care and medicine, tech companies have to cooperate with pre-existing regulations.
“There you have a regulatory environment you have to deal with and there’s more at stake,” Butterfield said. “You’ll see over the next couple of decades much larger changes that are just operating on a different type of time cycle.”
But we’re not going to be able to solve all the problems that are out there if only a small segment of our population has the opportunity to do so. That’s just one of the reasons why Butterfield has made diversity and inclusion a priority at Slack.
“I think the thing that we can really double or triple or quadruple down on is ensuring we don’t fail them once they arrive at the company,” Butterfield said. “I’m not sure that exact formulation of tech is hostile to diversity. I think tech lives inside of a society that still has a lot of systemic racism and doesn’t stop at the boundaries of the tech industry. But neither is it especially exacerbated by being around technology. But it is maybe exacerbated by the irrational decision making of people who are trying to make money.”
Butterfield went on to say that the irrational decision making is what leads companies to keep hiring computer science dropouts from Stanford and engage in other types of pattern-matching, which “creates a system that in the absence of deliberate conscious intentional effort, is going to perpetuate itself.”
It’s no secret that the price for design and development of websites are through the roof. If you fire a contractor or employee with a marketing or other related degree, you’re also paying a premium (albeit likely a worthwhile one if you must hire!). Website costs can range from putting a minor ding in the budget to becoming your CPA’s biggest nightmare. Did you know that there are many little details that not only can you do without, but your customers probably won’t even notice? A lot of businesses are paying for services that are completely unnecessary.
Whether you’ve had your site for a few days or a few years, there are always avenues to cut costs. Sure, no one likes to read the fine print (or showcase a lack of tech-savviness by asking your developer what “static” really means), but it’s important to know what you need, what you don’t, and where you can save. Let’s make things simple with an overview of what can be axed today.
Your .Com
Yes, it was tempting when securing that perfect domain name to add on that extra .org, .info, or even .us for just $5 more. Do you really need it? No. Unless you’re at the helm of a huge international company with hundreds of competitors at your heels, you only need one domain. Those few extra bucks can seriously add up over the years and can be better spent on that new coffee maker for the office. Employee morale goes a lot farther than a dusty domain collection.
However, different types of top-level domains, such as country-specific items like .in for India or .ca for Canada, are picking up steam. It’s a natural side effect thanks to a globalized market, and it might be worth your while to add these to the cart if you plan to target more than one specific country.
Custom Graphics
Do you feel the need for a custom graphic (or dozen) every time a new page or product is added? These JPEGs go for a pretty penny and are often overrated. Browse through the wide range of free stock photography available online. Long gone are the days of a small sampling of cartoon images for the budget-minded business owner. Chances are you’ll find exactly what you’re looking for at absolutely no cost. The one time you should invest in a customer graphic? Company logos, and it should be a onetime deal.
A Big Company to Update HTML
A lot of businesses view website companies kind of like an attorney – something you have to keep on retainer “just in case.” These monthly “maintenance” bills are often overkill. How much is your developer really maintaining after the site is up and running? If you’re depending on a professional (with professional fees) to add simple product images and HTML descriptions, you’re overpaying.
Believe it or not, even the most tech shy person can learn basic HTML. When designing, or redesigning, your website, ask the developer to create a user-friendly content system to update basic information. Still not feeling up to the challenge? It’s much more affordable to hire a part-time college student for simple ongoing update tasks. Compare about $10 per hour for a smart engineering undergrad to a $50+ per hour seasoned pro to do the exact same thing.
Car manufacturers will have difficulty demonstrating just how safe self-driving vehicles are because of what’s at the core of their smarts: machine learning.
“You can’t just assume this stuff is going to work,” says Phillip Koopman, a computer scientist at Carnegie Mellon University who works in the automotive industry.
Photo: David Paul Morris/Bloomberg/Getty Images
A member of the media test drives a Tesla Motors Model S equipped with Autopilot in Palo Alto, Calif., last fall.
Car manufacturers will have difficulty demonstrating just how safe self-driving vehicles are because of what’s at the core of their smarts: machine learning.
“You can’t just assume this stuff is going to work,” says Phillip Koopman, a computer scientist at Carnegie Mellon University who works in the automotive industry.
In 2014, a market research firm projected that the self-driving car market will be worth $87 billion by 2030. Several companies, including Google, Tesla, and Uber, are experimenting with computer-assisted or fully autonomous driving projects—with varying success because of the myriad technical obstacles that must be overcome.
Koopman is one of several researchers who believe that the nature of machine learning makes verifying that these autonomous vehicles will operate safely very challenging.
Traditionally, he says, engineers write computer code to meet requirements and then perform tests to check that it met them.
But with machine learning, which lets a computer grasp complexity—for example, processing images taken at different hours of the day, yet still identifying important objects in a scene like crosswalks and stop signs—the process is not so straightforward. According to Koopman, “The [difficult thing about] machine learning is that you don’t know how to write the requirements.”
Years ago, engineers realized that analyzing images from cameras is a problem that can’t be solved by traditional software. They turned to machine learning algorithms, which process examples to create mathematical models for solving specific tasks.
Engineers provide many human-annotated examples—say, what a stop sign is, and what isn’t a stop sign. An algorithm strips down the images, picking unique features and building a model. When a computer is subsequently presented with new images, it can run them through the trained model and get its predictions regarding which images contain a stop sign and which ones don’t.
“This is an inherent risk and failure mode of inductive learning,” Koopman says. If you look inside the model to see what it does, all you get are statistical numbers. It’s a black box. You don’t know exactly what it’s learning, he says.
To make things more concrete, imagine if you test drive your self-driving car and want it to learn how to avoid pedestrians. So you have people in orange safety shirts stand around and you let the car loose. It might be training to recognize hands, arms, and legs—or maybe it’s training to recognize an orange shirt.
Or, more subtly, imagine that you’ve conducted the training during the summer, and nobody wore a hat. And the first hat the self-driving car sees on the streets freaks it out.
“There’s an infinite number of things,” that the algorithm might be training on, he says.
Google researchers once tried identifying dumbbells with an artificial neural network, a common machine learning model that mimics the neurons in the brain and their connections. Surprisingly, the trained model could identify dumbbells in images only when an arm was attached.
Other problems with safety verification, Koopman says, include training and testing the algorithm too much on similar data; it’s like memorizing flash cards and regurgitating the information on an exam.
If Uber dropped its self-driving cars in a random city, he says, where it hasn’t exhaustively honed computer maps, then maybe they wouldn’t work as well as expected. There’s an easy fix: If you only train and only operate in downtown Pittsburgh (which Uber has mapped), then that could be okay, but it’s a limitation to be aware of.
There’s also the challenge of ensuring that small changes in what the system perceives—perhaps because of fog, dust, or mist—don’t affect what algorithms identify. Research conducted in 2013 found that changing individual pixels in an image, invisible to the unaided eye, can trick a machine learning algorithm into thinking a schoolbus is not a schoolbus.
“You would never put such [a machine learning] algorithm into a plane because then you cannot prove the system is correct,” says Matthieu Roy, a software dependability engineer at the National Center for Scientific Research in Toulouse, France, who has worked in both the automotive and avionics industries. If an airplane does not meet independent safety tests, it cannot take off or land, he says.
Roy says it would be too difficult to test autonomous cars for all the scenarios they could experience (think of an explosion or a plane crashing right in front). “But you have to cope with all the risks that may arrive,” he says.
Alessia Knauss, a software engineering postdoc at the Chalmers University of Technology in Göteborg, Sweden, is working on a study to determine the best tests for autonomous vehicles. “It’s all so costly,” she says.
She’s currently interviewing auto companies to get their perspectives. She says that even if there were multiple sensors—such as in Google’s cars—that act as backups, each component has to be tested based on what it does, and so do all of the systems that make use of it.
“We’ll see how much we can contribute,” Knauss says.
Koopman wants automakers to demonstrate to an independent agency why they believe their systems are safe. “I’m not so keen to take their word for it,” he says.
In particular, he wants car companies to explain the features of the algorithms, the representativeness of the training and testing data for different scenarios, and, ultimately, why their simulations are safe for the environments the vehicle is supposed to work in. If an engineering team simulated driving a car 10 billion miles without any hiccups, although the car didn’t see everything, a company could explain that any other scenarios wouldn’t happen very often.
“Every other industry that does mission critical software has independent checks and balances,” he says.
Last month, the U.S. National Highway Traffic Safety Administration unveiled guidelines for autonomous cars, but they make independent safety testing optional.
Koopman says that with company deadlines and cost targets, sometimes safety corners can be cut, such as during the 1986 NASA Challenger accident, where ignoring risk led to a spacecraft exploding 73 seconds after liftoff and killing seven astronauts.
It’s possible to have independent safety checks without publicly disclosing how the algorithms work, he says. The aviation industry has engineering representatives who work inside aviation companies; it’s standard practice to have them sign nondisclosure agreements.
“I’m not telling them how to do it, but there should be some transparency,” says Koopman.
Machine learning has the best chance of achieving meaningful return on investment when companies model previous success.
At last week’s Applied Artificial Intelligence conference in San Francisco, Uber’s Head of Machine Learning Danny Lange laid out his four principles for simplifying the process of applying machine learning in business.
Lange has witnessed firsthand the evolution of machine learning technologies, and he has a pretty good idea of what works and what doesn’t when companies want to implement machine learning for the first time.
A software creator and computer scientist by early trade, Lange founded Cupertino-based Vocomo Software in 2001 before it was acquired by Voxeo in 2005. Most recently in November 2015, Lange took on the role of head of machine learning at Uber.
One of the beauties of more companies implementing machine learning are all the mistakes they make and the resulting lessons that can be gleaned by those who are interested in using the technology, but haven’t yet made the leap.
You don’t have to be a behemoth company, Lange says, to apply machine learning. Open-source machine learning platforms are more accessible than ever, and if you have the right framework for implementing them, opportunities abound for even smaller businesses to find value. Lange suggests making the time spent implementing machine learning more productive by considering the following:
1 – ‘Low hanging fruit’ is the answer to this question: “If we only knew…”. Find a problem before you implement machine learning as a solution. Ask the question you’re dying to know but can’t figure out with existing methods i.e. ‘If we only knew’ the real return on investment (ROI) of our video marketing, or how to get more people to stay on our subscription software, or the commonalities of our customers that require the least amount of weekly and monthly maintenance, or (fill in the blank).
2 – Start supervised learning with a wealth of historic data. Lange argues that most companies don’t need to collect months of data after implementing a machine learning system before they derive value. Instead, look at the historical information that you already have and feed it to a supervised machine learning system (an algorithm that takes a known set of inputs and a matching known set of outputs and trains a model to generate predictions for responses to new data). Companies often have reams of saved customer service data that can yield lots of valuable insights, like how lead sources correlate to refunds, or how service packages are related to the amount of customer support a particular customer requires. The key is to choose existing data that is related to your main problem or question so that you drive ROI with purpose.
3 – Start with clean data, not big data: Don’t just find the biggest bucket of information; instead, find the information that you know is clean. Maybe you have lots of data around promotions and sales, but you tracked that data differently every month and yielded “messy” or “dirty” data (in other words, data that is not uniform). You have make sure you’re comparing apples to apples – this is what’s meant by clean data. Try finding a clean subset of information from that larger messy data set – maybe the way you measure and track customer churn and lead source has been the same. Your resulting dataset may not be as big, but if you can look at the data evenly across the board i.e. the format has stayed the same, then it’s considered clean and right for the job.
4 – Use an available cloud system (Amazon, Google, Microsoft, etc.): Some of the biggest names in the industry have started to introduced cloud-based machine learning (also known as open source software libraries), which are more or less like ‘machine learning kits’ that allow companies and developers of varying skill levels to build their own systems and models. Amazon offers Amazon Machine Learning, Google has TensorFlow, Baidu offers The Stack, and there are others. Danny recommends doing some research and leveraging one of these pre-packaged systems and skipping the from-scratch route.
Published on December 14, 2012 at https://www.simplilearn.com/prince2-vs-pmp-certifications-article
Prior to taking up a project management certification, professionals tend to grapple with the issue of choosing between the PMP and PRINCE2 certifications, two very reputed credentials in the project management field. This makes the question of which to invest in difficult to answer.
Worry not – we’ve done all the hard work for you! This article offers an overview of the two certifications and job prospects for both. Read on for more – and make the right choice!
What is the Basic Difference Between PMP & PRINCE2?
The PRINCE2 and PMP certifications involve two different project management frameworks. Both offer a body of knowledge and a proven approach to managing projects effectively. Picking one over the other is a decision that is a function of various factors. The ideal choice should be based on the industry or company you are aiming to build a career in, and the type of project one is leading or directing. Both the certification programs are of equal value, albeit in different aspects of project management.
Time-Out
The Project Management Professional (PMP) Certification
One of the most-reputed certifications in the world for project managers, the PMP or Project Management Professional Certification is a qualification program that is offered by the Project Management Institute (PMI). Various industries use PMP as a standard requirement for Project Managers. Becoming a PMP empowers you to work with any methodology and in any industry. The course covers a wide spectrum of project management techniques and competencies that are necessary for any project manager, as well as increasing your earning potential. This course, administered by PMI in the USA, has also gained popularity in Europe and Asia.
A few features of the PMP qualification include:
It is indicative of your proficiency in using the PMBOK, the Project Management Book of Knowledge.
It validates your knowledge of “generally-accepted” best practices and principles of project management.
The Projects IN Controlled Environments (PRINCE2) Certification
The PRINCE2, or ‘Projects IN Controlled Environments’ certification, is a process-based project management method that offers a systematic method for delivering a successful project with clear templates, processes, and steps. The certification is both, process and project focused. PRINCE2 is administered in the UK by the APMG.
A few features of PRINCE2 are as follows-
It is a broad, high-level, general framework of project management principles, which means it is recommended for and implemented on just about any kind of project.
It has a well-laid out and standardized approach to project management.
It spells out the roles and responsibilities of each member of the team tasked with managing the project.
It divides the master project plan into Project Plans, Stage Plans, and Team Plans, which eliminates ambiguity and makes it easier to execute the project.
It is both project and process-focused.
Industry Demand
If you wish to get one of these certifications, it is important that you do your research before you begin to walk down one path or the other. Each of the certifications is more popular in some geographies than others, making it important to determine which of the two certifications will boost your employability. In addition, a few industry sectors prefer the PRINCE2 certification, while others prefer the PMP certification.
The PMP certification is preferred in the USA, Canada, Middle East and Australia. PRINCE2, however, is recognized in UK, Europe, and Australia.
Salary Prospects
A number of studies and surveys show that certified professionals earn much more than their non-certified peers. Having a project management certification, especially ones like PMP and PRINCE2, signifies that an individual knows and understands the universal language of Project Management.
The PMI Project Management Salary Survey – Seventh Edition, states that the salaries of project managers around the world continue to climb.
In the US alone, the average annual salary of a certified PMP is $105,000 per year. They earn an average of 16% more than their non-certified peers. The highest salary a PMP can earn is in Switzerland, $160,409.
The average salary for professionals with a PRINCE2 certification is $77,540.
Benefits to Getting a PMP vs Getting a PRINCE2
Benefits of getting the PMP certification include:
Better Salary Prospects: Certified PMPs get larger and more frequent pay raises than their non-certified peers.
Networking Opportunities: To obtain a PMP certification, it is recommended that you become a member of PMI. Once you are a member, you have access to a vast network of professionals with similar qualifications. These contacts can prove crucial in helping you land a project management job.
Better Employment Prospects: As the world’s most popular project management certification, the PMP credential is recognized and trusted by employers worldwide.
Benefits of getting the PRINCE2 certification include:
Exhaustive Body of Knowledge: The PRINCE2 body of knowledge equips credential-holders with the tools to analyze a project from all angles, ensuring its viability before it is initiated. Factors like user requirements and potential risks are taken into consideration ensuring that a project that is bound to encounter hurdles is nippeds in the planning stages.
Well Laid-out Methodology: A lot of time and resource-usage is saved during the completion of a unique project since the certification has a clearly laid out methodology.
Standardization: Confusion in project execution is eliminated since the same, standard approach is used throughout, with common filing systems, procedures, and documents.
Improved Salary and Better Employability Prospects: There is a higher chance of landing a better job that pays well, in countries like the United Kingdom. Your employer is provided with enough justification of your skills to give you a pay raise.
Driven by Business Case: PRINCE2 requires users to self-assess and provide updates on business cases at defined points to ensure that a project will deliver value to the organization and its customers. Failure to do so will eliminate the justification for the continuity of the project.
The Exams
With a total of 200 questions, the PMP exam lasts four hours and is split into the areas of Project Initiation, Project Planning, Project Execution, Monitoring and Controlling, and Project Closing. Within these five larger domains are a multitude of other skills like risk identification, quality management, change management, materials management, and much more.
The PRINCE2 Foundation exam tests the individual with 75 multiple choice questions, for 60 minutes. Out of the 75 questions, 5 are trial based and not counted during the scoring process.
The Practitioner exam is an objective, scenario based paper. The individual is tested based on 9 questions with a time limit of 2.5 hours.
Prerequisites
There is a set of prerequisites that one will need to meet to take up the PMP exam.
Secondary degree (high school diploma, associate’s degree or the global equivalent)
7,500 hours leading and directing projects
35 hours of project management education
OR
A four-year degree
4,500 hours leading and directing projects
35 hours of project management education
There are no defined pre requisites for the PRINCE2 exams. However, it is recommended that you have some project management experience and formal training before you sit for the exam.The Practitioner exam will require you to have passed the Foundation exam.
The Exam Cost
The cost of the PMP exam – though a little expensive – is well worth the investment. For members of the PMI, it is $405, while for non-members it is $555.
The cost of the PRINCE to exam varies according to the region you are taking the exam in.
Maintaining your Credential
To maintain the PMP certification, you will need to attain 60 PDUs or Professional Development Units, every three years.
The PRINCE2 Foundation exam needs no renewal. The Practitioner exam, however, is valid for 5 years, after which it will become invalid. Professionals will have to complete and pass the PRINCE2 re-registration examination 3-5 years following their previous practitioner exam.
Is it advisable to do both?
Many people view the PRINCE2 and PMP certifications as different, which is why there is a battle between the two certifications.
The PMP is related to the knowledge and skills necessary to attain successfully manage a project and its delivery.
PRINCE2 is focused mainly on the processes and the framework to successfully execute the project.
Therefore, it is advisable that you do both of these certifications as it helps in the development of the professional’s all round Project Management skills.
There may be a few drawbacks to this-
Conflicting language: The two courses use different terminologies, which can be confusing at first. For example, project charter in PMP is what is known as project brief in PRINCE2.
Different Techniques: Some techniques differ vastly, such as the categorization of risk.
Conclusion
The certification you choose should depend on your job prospects and the region you are working in. PMP and PRINCE2 are not competing credentials in all regions. A professional would benefit from the skills and knowledge that both of these certifications offer.
