9 automotive design trends that need to die and soon

first_img 2020 BMW M340i review: A dash of M makes everything better More about 2019 Audi A6 Premium 45 TFSI quattro 22 Car Industry Audi More From Roadshow 2020 Hyundai Palisade review: Posh enough to make Genesis jealous 2020 Kia Telluride review: Kia’s new SUV has big style and bigger valuecenter_img Cars of the 1950s had their chrome. Vehicles from the ’80s were boxy. In the ’90s, everything got a little melty, like a candy bar left out in the sun. Whatever the decade, specific design trends proliferate across the entire auto industry.But they aren’t all good. Sure, today’s cars are really pushing the styling envelope, but that’s also leading to a number of questionable choices. Here are the modern automotive design trends that need to die, and soon.Light-up badgesI spend every day being assaulted by #brands. The last thing I need is a street full of cars, shouting their names at me in the night. Expressive design should work by itself. We don’t need to get hit over the head repeatedly by the badge. Plus, it invites higher repair costs when its driver inevitably gets distracted on Tinder and smashes into the pickup truck ahead of ’em.– Andrew KrokEnlarge ImageAh, the Mercedes illuminated star. It created a monster. Mercedes-Benz Massive grilles that are mostly closed offIt’s subjectively bad enough that automotive designers are locked in a weird arms race for the biggest grille, but then you get close and realize that, often, more than half of that grille is blocked off because there’s really no practical reason for a grocery-getter to have such a massive maw.– Antuan Goodwin2019 Toyota AvalonEnlarge ImageA vast majority of the Toyota Avalon’s huge grille is nonfunctional. Antuan Goodwin/Roadshow Fake ventsWhile performance affectations are almost kind of understandable on humble everyday cars, they’re particularly infuriating on high-performance automobiles. This trend amounts to bra or trouser stuffing, and it’s wholly unnecessary when a car still has “the goods.”– Chris PaukertLong-Term 2018 Kia Stinger GTEnlarge ImageThe Kia Stinger is a formidable performance car — but we hate its fake vents. Steven Ewing/Roadshow Jewel headlightsWhy are designers inspired by arachnids? When I look at a car I don’t want to be looking at a spider. Multiple light cubes in the housing are just design for design’s sake.– Emme HallAcura MDX PMC EditionEnlarge ImageMy, Acura MDX, what overly fancy headlights you have. Steven Pham/Roadshow Fake exhaust tipsThere are some slick-looking exhaust tips on cars these days, but the problem is that a lot of them aren’t real. In many cases it’s just a fancy outlet molded into the rear bumper with a regular round pipe behind it like on the Mercedes-AMG CLA45. And sometimes there’s not even a cutout at all, such as on the 2019 Audi A6. It’s just disappointing to see and it looks cheap.– Jon Wong2019-audi-a6-69Enlarge ImageThe outlets on this Audi A6? All fake. Jon Wong/Roadshow Asymmetrical wheelsIt’s great to have wild wheel designs, but when the wheels end up facing opposite directions on opposite sides of a car, it irks me no end.– Jake Holmes2018 Volkswagen Golf REnlarge ImageWe love the Volkswagen Golf R, but hate its asymmetrical wheels. Volkswagen Floating roofsThis is a stupid bit of design language because it interrupts the eye moving over a car. It’s unforgivable on any car, whether it’s a Nissan Murano or the otherwise gorgeous Aston Martin DB11.– Kyle Hyatt2019 Nissan MuranoEnlarge ImageNissan is doing the floating roof thing more than any other automaker. Emme Hall/Roadshow Coupe-oversAs far as I’m concerned, the word “coupe” is exclusively reserved for vehicles with two-doors — though I’ll make exceptions for the small suicide doors on the Mazda RX-8 and late ’90s and early 2000s Saturn SC. “Four-door coupe?” No. It’s called a sedan. But “coupe crossover?” Like, no. That’s not a thing.But beyond the inherent ugliness and pointlessness of these vehicles, I hate that automakers actually charge more for them than their equivalent, traditionally shaped brethren. You pay more to get less. And your car looks stupid.– Steven Ewing2020 Mercedes-Benz GLC CoupeEnlarge ImageIf it has four doors, it’s not a coupe. Mercedes-Benz Excessively low-profile tiresListen, I too love the look of a tire that’s barely thicker than a rubber band and has been stretched over the edge of a wheel large enough to qualify as an automotive caricature. I agree that it adds a lot of visual presence. But, spend a few minutes crossing a bumpy road on a wheel-and-tire package like that, and then do it again with something offering a higher rubber-to-metal ratio, and you’ll see that not every SUV on the road needs to be rolling on 22s wrapped with low-profile tires. Leave that to the supercars and go with something a little more practical on your next ride.– Tim StevensVolvo V90 R-DesignEnlarge ImageVolvo V90 R-Design: Great look, harsh ride. Volvo Originally published May 26, 2018. Preview • Tags Aston Martin Audi Kia Mazda Mercedes-Benz Nissan Toyota Volkswagen Volvo Acura Comments Share your voicelast_img read more

Suu Kyi cancels public speech in Australia as feeling unwell

first_imgMyanmar`s State Counsellor Aung San Suu Kyi (L) receives an official welcome on the forecourt during her visit to Parliament House in Canberra on 19 March 2018. Suu Kyi is in the Australian capital after attending the ASEAN (Association of Southeast Asian Nations)-Australia special summit in Sydney over the weekend. Photo: AFPMyanmar’s de facto leader Aung San Suu Kyi on Monday pulled out of a public speech and question-and-answer session in Sydney because she was “not feeling well”, the event’s organisers said.Suu Kyi has been under fire internationally for her public silence about a military crackdown in Myanmar’s Rakhine state that has seen nearly 700,000 of the Muslim Rohingya minority flee to Bangladesh.Suu Kyi, who attended a special ASEAN-Australia summit on Friday-Sunday, was in Canberra for talks with Prime Minister Malcolm Turnbull Monday. She had been due to make a keynote speech at the Lowy Institute think-tank in Sydney Tuesday.The speech and subsequent Q and A session would have been the only public comments the Nobel Prize winner would have made during her Australia trip.“This afternoon the Lowy Institute was informed by the Myanmar embassy that the State Counsellor will no longer be able to participate in this event as she is not feeling well,” a spokeswoman for the think-tank said in a statement.“Accordingly, the event is now cancelled.”The Rohingya humanitarian crisis was one of the key topics at the special summit between the Association of Southeast Asian Nations (ASEAN) and Australia, with other leaders quizzing her about the issue during a gathering Sunday, Turnbull said.Malaysia’s leader Najib Razak had warned Saturday that the issue could threaten regional security since those victimised could fall prey to extremist groups like the Islamic State.The exodus has sparked rare tension within the regional body, and Muslim-majority Malaysia has called for an independent ASEAN-led investigation into allegations of army abuse.Suu Kyi was the subject of public protests against human rights abuses during the summit, with demonstrators criticising her as well as Cambodian strongman Hun Sen and Vietnam’s Nguyen Xuan Phuc.last_img

Obama Says Dysfunction Under Trump Is Hurting US Security Prosperity

first_img To embed this piece of audio in your site, please use this code: 00:00 /03:55 Listen 00:00 /21:39 Obama noted other causes for that division, then he pointed to the costs. He said one of the biggest revelations to him when he took office was the degree to which the U.S. underwrites international order.“If there’s a problem around the world, people do not call Moscow,” he said. “They do not call Beijing. They call Washington. Even our adversaries expect us to solve problems and expect us to keep things running.”And here he took a swipe at the current administration. Without naming President Trump, Obama talked about the costs of “dysfunction” in Washington, making it difficult to make decisions, and the undermining of career civil servants particularly in the State Department.Mike Stravato for Rice University’s Baker InstituteFrom left to right: James Baker III, Former President Barack Obama and Edward Djerejian.“That doesn’t just weaken our influence. It provides opportunities for disorder to start ramping up all around the world and ultimately makes us less safe and makes us less prosperous,” Obama said.That led the evening’s moderator, historian Jon Meacham, to pivot the discussion to how U.S. foreign policy has shifted under the current administration. Secretary Baker blasted Trump’s attacks on the alliances and institutions that helped the U.S. win the Cold War.“This president is right in one respect for sure,” Baker said. “NATO needs to, our European allies need to pay their way, what they’ve agreed to pay, and we shouldn’t be required forever to pick up the tab on that. But these institutions make America stronger, and we ought not to be running them down.” Those included not only military alliances but also economic institutions.Obama echoed the point. But he noted that supporters of that global model, himself included, became “a little too comfortable.”“We did not adapt quickly enough to the fact that there were people being left behind,” he said.Others had, and the results started playing out during Obama’s tenure, shaping the outcome of the 2016 election, and driving policy ever since. “You start getting politics that based on, ‘That person’s not like me, and it must be their fault.’ And you start getting a politics based on a nationalism that’s not pride in country but hatred for somebody on the other side of the border,” he said.Towards the end of the evening, Obama reflected on his time in the Oval Office, saying he and his predecessors shared a reverence for the office independent of themselves. He declined to mention his successor. X Listen Andrew Schneider/Houston Public MediaFormer President Barack Obama, speaking at Rice University’s Baker InstituteFormer President Barack Obama visited Houston last night. He spoke at Rice University’s Baker Institute at an event marking the think tank’s 25th anniversary. Joining Obama on stage was former Secretary of State James Baker. Much of the event focused on the importance of bipartisanship and how that had broken down in the years between when Baker came to Washington and when Obama took office. The two agreed that the changing media landscape played a big part.“In 1981, your news cycle was still governed by the stories that were going to be filed by the AP, Washington Post, maybe New York Times, and the three broadcast stations,” Obama said. Whether people got their news from Walter Cronkite or David Brinkley, they tended to agree on a common set of facts. That set a baseline around which both parties had to adapt and respond to.“By the time I take office,” Obama said, “what you increasingly have is a media environment in which, if you are a Fox News viewer, you have an entirely different reality than if you are a New York Times reader.” To embed this piece of audio in your site, please use this code: X Sharelast_img read more

Elijah Cummings Hosts 18th Annual Job Fair

first_imgU.S. Rep. Elijah Cummings (D-Md.) is hosting his 18th Annual Job Fair from 9 a.m. to 2 p.m. on April 13 at the Fifth Regiment Armory, located at 219, 29th Division St. in Baltimore. The fair will feature various employment sectors and apprenticeship training programslast_img

Smart Money How Artificial Intelligence Will Transform Wealth Management

first_img Opinions expressed by Entrepreneur contributors are their own. Register Now » September 10, 2018 Rather than making it obsolete, artificial intelligence appears poised to revitalize the wealth management sector, ensuring that customers can rest easy knowing they are getting the benefit of fresh insights and streamlined processes. Far from taking the human element out of wealth management, it’ll let us personalize services even better, streamlining complex processes and making the business of handling clients’ wealth more effective and profitable for all.Related: Artificial Intelligence Is Likely to Make a Career in Finance, Medicine or Law a Lot Less LucrativeThere’s a reason a tech giant like IBM and its Watson AI are dipping their toes into wealth management — our field has a great deal of improvement at our fingertips, the kind that’ll lead to greater returns for both ourselves and our clients. While the full suite of services wealth managers offer will never be as simple as pushing a few buttons, those of us knee-deep in the complexities of wealth management work are about to get a valuable new tool. Here’s a look at just how AI will be changing the wealth management business in the coming years.Data analysisConsulting giant Deloitte named big data one of the biggest potential disruptors in wealth management, and it’s hard not to agree with that assessment. While we’ve always been able to draw on detailed information about our customers before, the massive amount of info we can now pull from is unprecedented and is sure to change wealth management for good. Thanks to AI’s enhanced data analysis capability, wealth managers will be able to design strategies that take a greater number of factors into account, informed by a deep dive into all the valuable data our software has been able to gather. Soon, catering plans to client’s every specification and pinpointing solutions with incredible accuracy will be a wholesale expectation for the job, not a nice bonus enjoyed only by the biggest firms.Related: 5 Reasons Machine Learning Is the Future of MarketingSupercharged investmentAlgorithms have already transformed the investment sphere, allowing investment firms and wealth managers to make huge numbers of transactions without lifting a finger. If these pre-programmed trades were the baby steps toward a smarter investment strategy, AI represents a full-on Usain Bolt sprint. Artificial intelligence-based investing gives money managers their own ultra-capable (and fast) research assistant, and as the technology evolves they’ll only get smarter. Analyzing past performances, adjusting portfolios on the fly, and presenting new streams of opportunity are just the beginning for AI investing strategies. When you’re in charge of a client’s future, you want to give them the best of your abilities. With AI, you’ll be doing that and more.Accounting precisionTomorrow’s wealth manager will be able to pass off the most tedious and repetitive accounting tasks to AI. The time savings alone are enough to get excited about, but it’s the reliability of artificial intelligence in making these crucial calculations that’s the real game changer. Needle-in-a-haystack accounting errors way well become a thing of the past, something that should come as a major relief to the money management sector. Not every accounting mistake leads to unmitigated disaster, but it’s a great weight off your shoulders to know there’s a technology-based solution to simple carelessness and mistakes. While nobody’s perfect, AI will help us get ever closer to that ideal.Related: Top 10 Best Chatbot Platform Tools to Build Chatbots for Your BusinessThe limits of AIWhile AI certainly represents a big step forward, it’s important to remember what it can’t yet do. Rest assured that smart machines won’t be completely eliminating the human element of wealth management (for now), but providing a powerful supplement to the work we already do. Jumping into AI is a great idea right now, but before putting all our eggs into this one basket it’s crucial to take note of the shortcomings of AI when it comes to the most important aspect of wealth management.When people put their signature on a wealth management agreement, a great deal of trust goes along with it. Anyone with some experience in this sector knows how important the human element is, that people need to buy into you personally just as much as they’re buying into your investment and accounting strategies. While AI is a powerful piece of software, it’s still just that: lines of code in a computer. It won’t be able to build relationships the way a personable manager can, or summarize complicated concepts in a way that only comes from experience. The talented wealth manager of today isn’t in danger of being replaced by AI, only supplemented by it.What this means for wealth managers of the near future is that tech-savvy will no longer be optional. The sooner we get used to this revolution, the faster we’ll be able to save time for ourselves and money for our clients. A complete understanding of what AI tools can and cannot do will be a necessary part of the job description. Essentially, we’ll be getting more time to focus on the big picture: And that means getting the best value for the people who entrust us with their lives’ earnings.AI isn’t a magic box that we can put all our problems into and spit out money, but it’s a tool more powerful than any we’ve seen before. When we can wield this exciting new tech tool intelligently and responsibly, wealth managers of all stripes stand to get a lot more done, for themselves and for their clients. For anyone hoping to succeed in this fast-moving industry, that’s something to celebrate. 5 min read Growing a business sometimes requires thinking outside the box. Free Webinar | Sept. 9: The Entrepreneur’s Playbook for Going Globallast_img read more

Sherin Thomas explains how to build a pipeline in PyTorch for deep

first_imgA typical deep learning workflow starts with ideation and research around a problem statement, where the architectural design and model decisions come into play. Following this, the theoretical model is experimented using prototypes. This includes trying out different models or techniques, such as skip connection, or making decisions on what not to try out. PyTorch was started as a research framework by a Facebook intern, and now it has grown to be used as a research or prototype framework and to write an efficient model with serving modules. The PyTorch deep learning workflow is fairly equivalent to the workflow implemented by almost everyone in the industry, even for highly sophisticated implementations, with slight variations. In this article, we explain the core of ideation and planning, design and experimentation of the PyTorch deep learning workflow. This article is an excerpt from the book PyTorch Deep Learning Hands-On by Sherin Thomas and Sudhanshi Passi. This book attempts to provide an entirely practical introduction to PyTorch. This PyTorch publication has numerous examples and dynamic AI applications and demonstrates the simplicity and efficiency of the PyTorch approach to machine intelligence and deep learning. Ideation and planning Usually, in an organization, the product team comes up with a problem statement for the engineering team, to know whether they can solve it or not. This is the start of the ideation phase. However, in academia, this could be the decision phase where candidates have to find a problem for their thesis. In the ideation phase, engineers brainstorm and find the theoretical implementations that could potentially solve the problem. In addition to converting the problem statement to a theoretical solution, the ideation phase is where we decide what the data types are and what dataset we should use to build the proof of concept (POC) of the minimum viable product (MVP). Also, this is the stage where the team decides which framework to go with by analyzing the behavior of the problem statement, available implementations, available pretrained models, and so on. This stage is very common in the industry, and I have come across numerous examples where a well-planned ideation phase helped the team to roll out a reliable product on time, while a non-planned ideation phase destroyed the whole product creation. Design and experimentation The crucial part of design and experimentation lies in the dataset and the preprocessing of the dataset. For any data science project, the major timeshare is spent on data cleaning and preprocessing. Deep learning is no exception from this. Data preprocessing is one of the vital parts of building a deep learning pipeline. Usually, for a neural network to process, real-world datasets are not cleaned or formatted. Conversion to floats or integers, normalization and so on, is required before further processing. Building a data processing pipeline is also a non-trivial task, which consists of writing a lot of boilerplate code. For making it much easier, dataset builders and DataLoader pipeline packages are built into the core of PyTorch. The dataset and DataLoader classes Different types of deep learning problems require different types of datasets, and each of them might require different types of preprocessing depending on the neural network architecture we use. This is one of the core problems in deep learning pipeline building. Although the community has made the datasets for different tasks available for free, writing a preprocessing script is almost always painful. PyTorch solves this problem by giving abstract classes to write custom datasets and data loaders. The example given here is a simple dataset class to load the fizzbuzz dataset, but extending this to handle any type of dataset is fairly straightforward. PyTorch’s official documentation uses a similar approach to preprocess an image dataset before passing that to a complex convolutional neural network (CNN) architecture. A dataset class in PyTorch is a high-level abstraction that handles almost everything required by the data loaders. The custom dataset class defined by the user needs to override the __len__ and __getitem__ functions of the parent class, where __len__ is being used by the data loaders to determine the length of the dataset and __getitem__ is being used by the data loaders to get the item. The __getitem__ function expects the user to pass the index as an argument and get the item that resides on that index: from dataclasses import dataclassfrom torch.utils.data import Dataset, DataLoader@dataclass(eq=False)class FizBuzDataset(Dataset):    input_size: int    start: int = 0    end: int = 1000    def encoder(self,num):        ret = [int(i) for i in ‘{0:b}’.format(num)]        return[0] * (self.input_size – len(ret)) + ret    def __getitem__(self, idx):        x = self.encoder(idx)        if idx % 15 == 0:            y = [1,0,0,0]        elif idx % 5 ==0:            y = [0,1,0,0]        elif idx % 3 == 0:            y = [0,0,1,0]        else:            y = [0,0,0,1]        return x,y           def __len__(self):        return self.end – self.start The implementation of a custom dataset uses brand new dataclasses from Python 3.7. dataclasses help to eliminate boilerplate code for Python magic functions, such as __init__, using dynamic code generation. This needs the code to be type-hinted and that’s what the first three lines inside the class are for. You can read more about dataclasses in the official documentation of Python (https://docs.python.org/3/library/dataclasses.html). The __len__ function returns the difference between the end and start values passed to the class. In the fizzbuzz dataset, the data is generated by the program. The implementation of data generation is inside the __getitem__ function, where the class instance generates the data based on the index passed by DataLoader. PyTorch made the class abstraction as generic as possible such that the user can define what the data loader should return for each id. In this particular case, the class instance returns input and output for each index, where, input, x is the binary-encoder version of the index itself and output is the one-hot encoded output with four states. The four states represent whether the next number is a multiple of three (fizz), or a multiple of five (buzz), or a multiple of both three and five (fizzbuzz), or not a multiple of either three or five. Note: For Python newbies, the way the dataset works can be understood by looking first for the loop that loops over the integers, starting from zero to the length of the dataset (the length is returned by the __len__ function when len(object) is called). The following snippet shows the simple loop: dataset = FizBuzDataset()for i in range(len(dataset)):    x, y = dataset[i]dataloader = DataLoader(dataset, batch_size=10, shuffle=True,                     num_workers=4)for batch in dataloader:    print(batch) The DataLoader class accepts a dataset class that is inherited from torch.utils.data.Dataset. DataLoader accepts dataset and does non-trivial operations such as mini-batching, multithreading, shuffling, and so on, to fetch the data from the dataset. It accepts a dataset instance from the user and uses the sampler strategy to sample data as mini-batches. The num_worker argument decides how many parallel threads should be operating to fetch the data. This helps to avoid a CPU bottleneck so that the CPU can catch up with the GPU’s parallel operations. Data loaders allow users to specify whether to use pinned CUDA memory or not, which copies the data tensors to CUDA’s pinned memory before returning it to the user. Using pinned memory is the key to fast data transfers between devices, since the data is loaded into the pinned memory by the data loader itself, which is done by multiple cores of the CPU anyway. Most often, especially while prototyping, custom datasets might not be available for developers and in such cases, they have to rely on existing open datasets. The good thing about working on open datasets is that most of them are free from licensing burdens, and thousands of people have already tried preprocessing them, so the community will help out. PyTorch came up with utility packages for all three types of datasets with pretrained models, preprocessed datasets, and utility functions to work with these datasets. This article is about how to build a basic pipeline for deep learning development. The system we defined here is a very common/general approach that is followed by different sorts of companies, with slight changes. The benefit of starting with a generic workflow like this is that you can build a really complex workflow as your team/project grows on top of it. Build deep learning workflows and take deep learning models from prototyping to production with PyTorch Deep Learning Hands-On written by Sherin Thomas and Sudhanshu Passi. Read Next F8 PyTorch announcements: PyTorch 1.1 releases with new AI tools, open sourcing BoTorch and Ax, and more Facebook AI open-sources PyTorch-BigGraph for faster embeddings in large graphs Top 10 deep learning frameworkslast_img read more

Transat releases new Weddings brochure with special Weddingbells feature

first_img MONTREAL – Destination wedding planning is in full swing, made all the more easier with Transat’s new 2017-18 Weddings brochure, available now.New this year is a 17-page special feature by ‘Weddingbells’ that presents the hottest wedding dresses, the most beautiful tropical flower arrangements and tips on how to best capture the special day in photos. There’s also a wedding checklist, a comparison between destination weddings and weddings at home, wedding requirements for each destination and much more.Transat offers a number of advantages and perks for wedding couples, including an upgrade to Option Plus with Air Transat (with a compartment reserved for the wedding gown), a $500 future travel voucher, and a $100 credit for pre-booked excursions.Plus, the first 30 wedding guests who book with Transat will not be required to pay a deposit until 90 days after the booking date or 60 days before departure, whichever comes first.For couples looking for a more off-beat ceremony, a cruise has become an increasingly popular option. Today’s luxury liners offer venues that rival some of the best on land, in addition to providing complimentary wedding planning and a variety of inclusions. Even more, Transat’s new one-stop cruise packages include roundtrip flight, transfers and cruise.More news:  Beep, beep! Transat hits the streets with Cubamania truckGroups who book with Transat will also benefit from its Price Drop Guarantee, which states that should a package become available at a lower price than the one paid, Transat will refund the difference. Posted by Travelweek Group Tags: Transat, Wedding, Weddings Transat releases new Weddings brochure with special ‘Weddingbells’ featurecenter_img Share Thursday, November 3, 2016 << Previous PostNext Post >>last_img read more