A decade into the modern electric-car era one thing is clear: easy access to charging is vital to convince more drivers to make the leap to battery power from gasoline. So to ensure a smooth launch for its much-anticipated Taycanelectric sports car Porsche is taking a page from Tesla’s playbook and ensuring customers have access a coast-to-coast network of fast-charge stations.
The German luxury company said it will offer three years of charging at hundreds of stations operated across the U.S. with Volkswagen-owned Electrify America LLC as part of Taycan’s purchase price, which starts at about $80,000. Additionally, fast-charge stations will be installed at all of its 191 dealerships across the country, while customers will also be able to purchase Porsche home-charging systems, the company said.
“Together, Electrify America and our Porsche dealer network will provide a national infrastructure for DC fast charging that frees future Taycan owners from range anxiety,” Klaus Zellmer, president and CEO of Porsche Cars North America, said in a statement. “Porsche home charging technology will turn the customer’s garage into the equivalent of a personal gas station.”
Tesla has dominated the market for high-end electric vehicles since it began delivering $100,000 Roadsters a decade ago, but faces an onslaught of competition from new entries including the Taycan, Audi’s e-tron and Jaguar’s I-Pace, with more on the way from Cadillac and others. Porsche last week said it’s boosting production plans for the Taycan owing to higher demand than initially planned.
Some analysts suspect the competition from those new premium EVs, priced between $60,000 and $90,000, were behind Tesla’s recent decision to discontinue its entry-level 75D versions of the Model S sedan and Model X crossover to focus mainly on pricier versions selling for about $100,000 or more. Tesla’s core vehicle, the Model 3 sedan, starts at $44,000 and can cost more than $60,000 with all options.
Details of Porsche’s sleek four-door coupe are limited so far, though it will be a juiced up performance car offering more than 600-horsepower and over 300 miles of driving range per charge.
For three years Taycan buyers will get unlimited 30-minute charging sessions at Electrify America facilities that include more than 300 highway stations in 42 U.S. states and more than 180 stations in 17 cities, Porsche said. Each location will have an average of five charging bays, with some having up 10. The highway stations will have at least two 350 kW chargers per site, with additional chargers delivering up to 150kW.
The initial phase of the network, at 484 locations with more than 2,000 charging dispensers, will be installed or under construction by July 1, prior to Taycan’s launch in late 2019, Porsche and Electrify America said. Porsche dealers will spend about $70 million for fast-charge kiosks at their stores, the company said.
Tesla early on baked in a charging network to support its Model S when it launched the car in 2012. Initially, powering up at its Supercharger stations was also free, though over time it began charging customers as its owner base grew. Last week it moderated a planned increase in charging rates that Tesla fan site Electrek estimated would go up by about 33% per kilowatt hour.
-Alan OhnsmanForbes Staff
Faster, More Accurate Diagnoses: Healthcare Applications Of AI Research
When Google DeepMind’s AlphaGo shockingly defeated legendary Go player Lee Sedol in 2016, the terms artificial intelligence (AI), machine learning and deep learning were propelled into the technological mainstream.
AI is generally defined as the capacity for a computer or machine to exhibit or simulate intelligent behaviour such as Tesla’s self-driving carand Apple’s digital assistant Siri. It is a thriving field and the focus of much research and investment. Machine learning is the ability of an AI system to extract information from raw data and learn to make predictions from new data.
Deep learning combines artificial intelligence with machine learning. It is concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep learning has received much attention lately both in the consumer world and throughout the medical community.
Interest in deep learning surged with the success of AlexNet, a neural network designed by Alex Krizhevsky that won the 2012 ImageNet Large Scale Visual Recognition Challenge, an annual image classification competition.
Another relatively recent advancement is the use of graphical processing units (GPUs) to power deep learning algorithms. GPUs excel at computations (multiplications and additions) needed for deep learning applications, thereby lowering application processing time.
In our lab at the University of Saskatchewan we are doing interesting deep learning research related to healthcare applications — and as a professor of electrical and computer engineering, I lead the research team. When it comes to health care, using AI or machine learning to make diagnoses is new, and there has been exciting and promising progress.
Extracting blood vessels in the eye
Detecting abnormal retinal blood vessels is useful for diagnosing diabetes and heart disease. In order to provide reliable and meaningful medical interpretations, the retinal vessel must be extracted from a retinal image for reliable and meaningful interpretations.
Although manual segmentation is possible, it is a complex, time-consuming and tedious task which requires advanced professional skills.
My research team has developed a system that can segment retinal blood vessels simply by reading a raw retinal image. It is a computer-aided diagnosis system that reduces the work required by eye-care specialists and ophthalmologists, and processes images 10 times faster, while retaining high accuracy.
Detecting lung cancer
Computer tomography (CT) is widely used for lung cancer diagnosis. However, because visual representations of benign (non-cancerous) and malignant (cancerous) lesions in CT scans are similar, a CT scan cannot always provide a reliable diagnosis. This is true even for a thoracic radiologist with many years of experience.
The rapid growth of CT scan analysis has generated a pressing need for advanced computational tools to assist radiologists with the screening progress.
To improve radiologists’ diagnostic performance, we have proposed a deep learning solution. Based on our research findings, our solution outperforms experienced radiologists. Moreover, using a deep learning-based solution improves diagnostic performance overall and radiologists with less experience benefit from the system the most.
Limitations and challenges
Although great promise has been shown with deep learning algorithms in a variety of tasks across radiology and medicine, these systems are far from perfect. Obtaining high-quality annotated datasets will remain a challenge for deep learning training. Most computer vision research is based on natural images, but for healthcare applications, we need large annotated medical image datasets.
Another challenge from a clinical standpoint will be the time to test how well deep learning techniques perform in contrast to human radiologists.
There needs to be more collaboration between physicians and machine learning scientists. The high degree of complexity of human physiology will also be a challenge for machine learning techniques.
Another challenge is the requirements to validate a deep learning system for clinical implementation, which would likely require multi-institutional collaboration and large datasets. Finally, an efficient hardware platform is required to ensure fast processing of deep learning systems.
In the complex world of healthcare, AI tools can support human practitioners to provide faster service and more accurate diagnoses, and analyze data to identify trends or genetic information that may predispose someone to a particular disease. When saving minutes can mean saving lives, AI and machine learning may be transformative for healthcare workers and patients.
–Seokbum Ko; Professor, University of Saskatchewan
Here’s How The US Claims The Assange-Manning Conspiracy Worked
The U.S. government has disclosed more of its case against WikiLeaks cofounder Julian Assange. It hinges on a claim he and Chelsea Manning worked together to crack a password for a computer storing sensitive government files.
An affidavit unsealed Monday outlining the case against Assange said he conspired with Manning when they discussed working together to crack a password “related to two computers with access to classified national security information.” More specifically, the password belonged to a user called FTP (not to be confused with an FTP server) on two Windows computers that Manning could access from a base in Iraq, the government said.
The FTP account wasn’t associated with any specific individual, and the government alleged that if Manning had used it to pilfer files and hand them over to Wikileaks, she could have foiled investigators looking into who was behind the leaks.
“Although there is no evidence that the password to the FTP user was obtained, had Manning done so, she would have been able to take steps to procure classified information under a username that did not belong to her,” the affidavit read. “Such measures would have frustrated attempts to identify the source of the disclosures to WikiLeaks.”
The alleged conspiracy to crack the password took place in March 2010, two months after she’d walked out of the Iraq base with classified war reports from Iraq and Afghanistan. She was later convicted and served seven years in jail for downloading tens of thousands U.S. military documents and diplomatic cables.
How passwords are cracked
The reason any password had to be cracked in the first place was the use of what’s known as a “hash.” Microsoft’s Windows operating system doesn’t store passwords in plain text. That’s to prevent hackers who find a way on to the computer from seeing and stealing them. Instead, Microsoft makes life harder for cybercriminals and snoops by turning that plain text into scrambled code. That string of letters and numbers is known as a “hash value” and it’s created when an algorithm is applied to the plain text of the password.
For an attacker to get at the plain text it’s possible to do a so-called “brute force attack.” The process for this is basic: The hacker creates a huge list of guessed passwords through the same hashing algorithm used by Windows to find a matched hash value for the hidden password. Once the same hash value is calculated, the password has been found.
Sometimes a password will be too complex for guessing to work in a short enough time frame. That’s where “rainbow tables” come in. These contain a massive number of hash values for previously calculated passwords. Hackers use them to do a quick comparison of the hash they have with the ones in the table, in the hopes that it’s already been seen before and a match is available.
“In computing terms we call this a time/memory trade-off. Rather than spend time on a task, we pre-calculate parts of it and store them somewhere, essentially trading time for memory,” says Tom Wyatt, senior penetration tester at cybersecurity provider Bulletproof. “These tables can be calculated or downloaded from various online sources, and it simply boils down to paying for storage for it all; even in 2010 this was fairly cheap and entirely possible.”
But Microsoft goes one step further in protecting those hash values by splitting them in two, storing the parts in separate files. Here’s where a little trick comes in handy: A hacker might be able to recover those two separate pieces by rebooting a Windows PC using a CD with the Linux operating system. Back in 2010, it was possible to do that and recover the full hash value.
Ken Munro, a penetration tester with Pen Test Partners, told Forbes the technique still works, as long as there’s no additional layer of security over it, such as full disc encryption. “Whilst the technique still works, it’s quite rare to find systems that don’t now have full disc or similar encryption,” he added. (Microsoft hadn’t responded to a request for comment at the time of publication). According to the government’s telling of the story, evidence suggests Manning tried, and very possibly failed, with this technique. In a footnote in the affidavit, the government said Manning hadn’t provided Assange with the full hash, only one of the two halves required.
It’s alleged Manning passed what she thought was a hash value to Assange. The Wikileaks chief then said he would pass it on to a specialist in cracking, according to chats over the Jabber encrypted communications app, as provided in the affidavit. But, as per the investigators’ claims, there was some confusion: Manning said she wasn’t even sure what she handed to Assange was the hash value they wanted. Assange messaged Manning to ask if there were “any more hints” about the hash and that he’d had “no luck so far,” according to the government account. From there it’s unclear what happened. The government admits it didn’t know whether the password was ever cracked.
Not that it changes much for Assange: The charge is that of conspiracy. If he did offer assistance to help Manning gain access to U.S. government systems and encouraged the then intelligence analyst to leak files, the charge still stands. Manning, who served seven years in jail before being pardoned by President Barack Obama, is back behind bars for refusing to testify in the investigation into Wikileaks. Her lawyer had not responded to a request for comment at the time of publication.
Assange’s lawyer, Jennifer Robinson, couldn’t be reached for comment at the time of publication. She told Sky News yesterday that the indictment against her client showed “the kinds of communications journalists have with sources all the time.” Following Assange’s arrest, however, various journalists have said on Twitter that any incitement to hack organizations or steal documents was far from normal and risked breaking the law.
Meanwhile, the fallout from Assange’s arrest continues. According to Reuters, Ecuador’s telecommunications vice minister Patricio Real said the government’s networks had been hit by a mass of cyberattacks after it decided to revoke Assange’s asylum status. He claimed various government websites had been slammed by 40 million hacking attempts per day, double the number it typically sees.
-Thomas Brewster; Forbes Staff
10 Rules Of Email That Will Reduce Your Stress Levels
Email and smart phones can be stressful. Academics are calling this constant work connection “technostress”. Consequently, many European countries are now offering employees the “right to disconnect”.
The way email is used is complex, it cannot simply be labelled as “good” or “bad” and research shows that personality, the type of work people do and their goals can influence the way they react to email.
Good practice with email use is not just about limiting the amount of emails sent, but improving the quality of communication.
Here are ten tips to reduce the stress of email at work:
1. Get the subject line right
Use clear and actionable subject lines.
The subject line should communicate exactly what the email is about in six to ten words, to allow the recipient to prioritise the email without even opening it. On mobile devices, many people only see the first 30 characters of a subject line. So keep it short. But make it descriptive enough to give an idea of what the email is about from just the subject line.
2. Ask yourself: is email the right medium?
Are you in the same office? Could you go and speak to the person? Could you call? Often these other forms of communication can avoid the inefficient back and forth of emailing.
Instant messaging and video calling platforms like Slack and Skype could be more appropriate for quick internal back and forth messaging. Also, remember that most of the advice below applies to all types of electronic communication.
3. Don’t email out of office hours
Research shows that out-of-hours emails make it harder for people to recover from work stress.
Try and influence your company culture by avoiding sending or replying to emails outside your normal working hours.
Management should lead by example and avoid contacting their staff outside of their normal working hours. Some workplaces even switch off email access to employees out of hours. Consider implementing this while keeping a backup phone system for emergency contact only.
New research has also shown that just the expectation of 24-hour contact can negatively affect employee health.
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4. Use the delay delivery option
Some people like integrating their work and family lives and often continue working from home during their off-job time. If you are one of these people, or if you work across time zones, consider using the delay delivery option so your emails do not send until the next working day and do not interfere with other people’s off-job time.
5. Keep it positive
Think about the quality of email communication. Not just the quantity. Changes to email use should also focus on the quality of what is being sent and take into consideration the emotional reaction of the recipient.
Research suggests that conflicts are far easier to escalate and messages to be misinterpreted when communicated via email. Therefore, if it is bad news, think back to rule #2: is email the right medium?
6. Try ‘no email Friday’
In order to shift company culture and get people thinking about other methods of communication than email, try a “no email Friday” on the first Friday of every month, or maybe even every week. This is an initiative suggested by experts from the National Forum for Health and Wellbeing at Work, and is being used by businesses around the globe. Employees are encouraged to arrange face-to-face meetings or pick up the phone – or just get on top of the many emails they already have in their inbox on that day.
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7. Make your preferences known
Research has shown that not only too much but also too little email can cause stress due to a mismatch between the communication preferences of different people. Some people may like being emailed and cope much better with high email traffic than other means of communication. For these people, reducing the amount of emails they receive may cause more stress than it alleviates.
So consider people’s individual differences and make yours known. Add your preferred contact preferences to your email signature whether it is email, text or instant messages or a phone call.
8. Consider a holiday ‘bounce back’
Having a backlog of emails that builds up over the week appears to be one of the most commonly mentioned sources of technostress for workers. Think about setting up a system where emails are bounced back to the sender when someone is on holiday, with an alternative contact email for urgent requests. This would let you come back to a manageable inbox.
9. Have a separate work phone
Make this the only mobile device you can access work emails on, which gives you the freedom to switch it off after work hours. Also consider turning off email “push” (this is where your email server sends each new email to your phone when it arrives at the server) and instead choose a regular schedule (such as once per hour) for emails to be delivered to your phone (this also increases battery life).
10. Avoid late night screen time
Research suggests that late night smart phone use reduces our ability to get to sleep and also leads to constant thoughts and stress about work. This in turn reduces your sleep quality. Make the bed a phone-free zone to improve your sleep hygiene.
-Ricardo Twumasi; Lecturer in Organisational Psychology, University of Manchester
-Cary Cooper; 50th Anniversary Professor of Organisational Psychology and Health, University of Manchester
–Lina Siegl; PhD Researcher, University of Manchester
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