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Archivo del Autor: Belen De Leon

Kylie Jenner beats Zuck as youngest billionaire video – CNET

The reality star bumps Facebook founder Mark Zuckerberg off the top of the list, amassing $1B at the ripe old age of 21.
Source: CNET

OptiShokz headphone sunglasses available for preorder – CNET

AfterShokz, the maker of bone-conduction headphones, brings bone-conduction audio to sports glasses with its upcoming OptiShokz Revvez.
Source: CNET

Startup Law A to Z: Customer Contracts

Your startup needs customers to survive. If and when you make sales or generate installs, you are wading into the fast moving stream of commerce and exposing yourself to risk. Well-drafted customer contracts limit your liability and create legally enforceable rights to get paid for your work. In fact, contracts are actually dispute prevention mechanisms, forcing parties on either side to clearly define what is supposed to happen in advance, aligning expectations and increasing the likelihood that all goes according to plan.

So developing a working understanding of contracts generally, a deep understanding of your core customer contracts specifically, and hiring a competent lawyer to draft key contracts from the beginning, together represent an investment that will pay dividends over the life of your startup.

This article, the third in Extra Crunch’s exclusive “Startup Law A to Z” series, follows previous articles on intellectual property (IP) and corporate matters. If you are tuning in now, this series is designed to provide founders enough information to intelligently analyze business circumstances vis-à-vis certain common legal issues startups face. These articles are detailed and admittedly lengthy, but the concepts discussed are critical for founders to understand deeply.

If after reading this or other articles in the “Startup Law A to Z” series, you identify legal risks facing your startup, then other Extra Crunch resources can help. For example, the Verified Experts of Extra Crunch include detailed profiles of “Verified Expert Lawyers” – some of the most experienced and skilled startup lawyers in practice today. You can and should use these resources to identify attorneys focused on serving companies at your stage with experience in the particular matters at hand and simply reach out for further guidance.

The Customer Contracts checklist:

Contract Law Generally

  • Contract Formation
  • Term and Termination
  • Breach and Remedies

Terms of Use vs. End User License Agreements

  • Distinctions and Key Provisions
  • Enforceability through Click-Wrap Agreements
  • Notice of Amendments and Revisions

Privacy Policies

  • State, Federal, International Laws:
    • CCPA
    • CalOPPA
    • Required under Cal. Bus. & Prof. Code § 22575(a)
    • FTC (COPPA, HIPAA, Gramm-Leach-Bliley Act)
    • GDPR
  • Disclosure and Enforceability

NDAs

  • Mutual vs. One-Way
  • Definition of Confidential Information
  • Residual Clauses
  • Non-Solicit and Non-Competes

Master Services Agreements and Service Level Agreements

  • Y-Combinator “Sales Agreement” / MSA Template
  • Deal Terms, Legal Terms, Boilerplate Terms
  • Legal Terms Explained:
    • Warranty and Disclaimer
    • Indemnity (and Insurance)
    • Limitation of Liability

Contract law generally

What is a contract? Any law student preparing for the bar exam will tell you, in monotone: “a contract is a promise or set of promises, for breach of which the law provides a remedy, or the performance of which the law recognizes as a duty.”

Simple enough, but which law? For contracts, it is primarily the “common law” which governs contracts, that is, law derived from judicial decisions and not government-enacted statutes (historical background courtesy of UC Berkeley). That said, contracts for the sale of “goods” are governed by specifically promulgated rules set forth in the Uniform Commercial Code (or “UCC”). And yes, in certain circumstances, courts have found that software may be considered “goods” for this purpose, see On Contracts.


Source: TechCrunch

Las muertes por sobredosis podrían ser cosa del pasado con esta 'app'

El sistema monitoriza la respiración del usuario y emite una alerta en caso de que identifique síntomas. En las pruebas piloto ha demostrado mucha precisión y los voluntarios han mostrado interés por seguir usándola. El reto será convencer al resto de las personas en riesgo para que la usen
Source: MIT

Así trabajaba la legión de cuentas que provocó el colapso de Mt. Gox

Un análisis de las transacciones realizadas antes de que la plataforma de intercambio de bitcoins se declarara en bancarrota revela muchas actividades sospechosas, como transacciones que compraban criptomonedas a precios muy por debajo o muy por encima de su valor de referencia
Source: MIT

10 libros que Bill Gates leyó para elegir las 10 Tecnologías Emergentes

Antes de hablar sobre un tema, al antiguo CEO de Microsoft le gusta documentarse leyendo libros relacionados. De todos los que consultó para seleccionar la lista de las 10 Tecnologías Emergentes 2019, estos son sus 10 títulos favoritos
Source: MIT

Clever Tool Uses Apple’s Videogame Logic Engine to Protect Macs

A new Mac security service called GamePlan uses a system’s own indicators, and some videogame magic, to keep a lookout.
Source: Wired

Elon Musk wasn’t wrong about automating the Model 3 assembly line — he was just ahead of his time

In 2017, when Tesla announced incredibly ambitious Model 3 production targets of 5,000 Model 3s per week and the beginning of “production hell,” analysts were wary. But Elon Musk insisted he could pull it off, citing hyper-automation — a robotic assembly line — as his secret weapon to increase manufacturing speed and drive down costs. Fast-forward a year and a half and Tesla delivered 91,000 vehicles in Q4 2018. But the ramp-up didn’t come without massive issues and a move away from Musk’s original vision of a highly automated assembly line.

What happened?

Asked why the push toward automation didn’t pan out, Elon’s answer revolved around one major issue: robotic vision, or the software that controls what the assembly line robots can “see” and then do based on that computer vision. Unfortunately, the assembly line robots just couldn’t deal with unexpected orientations of objects like nuts and bolts, or complicated maneuvering between the car frame. Every such issue would cause the assembly line to stop. In the end, it was far easier to substitute humans for robots in many assembly situations.

Today, computer vision (the umbrella term for robotic vision) is everywhere and represents the next frontier of AI technologies and groundbreaking applications across a variety of industries. The advances being made right now by researchers and companies in the space are impressive and represent the missing pieces needed to make Elon Musk’s vision of an automated car assembly line a reality. At its core, these advances will give computers and robots the ability to reliably deal with the vast array of unexpected corner cases — those errant nuts and bolts — that occur in the real world.

A watershed moment in computer vision

Computer vision experienced a watershed moment in 2012 with the application of convolutional neural networks. Since then, it has really picked up steam. Before 2012, computer vision was largely about hand-crafted solutions — basically, algorithms had manually defined rule sets and could mathematically describe features of an image relatively effectively. These were hand-selected and then combined by a computer vision researcher in order to identify a specific object in an image, like a bicycle, a storefront or a face.

The rise of machine learning and advances in artificial neural nets changed all of that, allowing us to develop algorithms using massive amounts of training data that can automatically decipher and learn image features. The net effect of this was twofold: (1) solutions became much more robust (e.g. a face could still be identified as a face, even if it were oriented slightly differently, or in shadow), and (2) the creation of good solutions became reliant upon large amounts of high-quality training data (models learn features based on the training data, so it is critical that the training data is accurate, sufficient in quantity and represents the full diversity of situations the algorithm may later see).

Now in the lab: GANs, unsupervised learning and synthetic data

Next, new approaches like GANs (Generative Adversarial Networks), unsupervised learning and synthetic ground truth offer the potential to substantially reduce both the amount of training data required to develop high-quality computer vision models, as well as the time and effort required to collect the data. With these approaches, networks can actually bootstrap their own learning and identify corner cases and outliers with higher fidelity, far faster. Humans can then evaluate the corner cases to refine solutions and get to a high-quality model much more quickly.

These new approaches are rapidly expanding the envelope of computer vision in terms of applications, robustness and reliability. Not only do they hold the promise to solve Mr. Musk’s manufacturing challenges, but they will also dramatically extend the boundaries in myriad critical applications, some of which are highlighted below:

  • Manufacturing Automation: Robots will increasingly have the capability to deal with objects at randomized orientations, like a car seat that is 20 degrees off-center or a screw that is an inch too far to the left. Even further, robots will be able to reliably identify soft, flexible, transparent objects (think about, for example, the plastic bag of socks you ordered on Amazon last week). New robotics providers like Berkshire Grey are at the cutting edge of this.

  • Facial Detection: Previously, facial detection was not robust in corner cases like side angles, partial shading or occlusion, or babies’ faces. Now, researchers are finding that computer vision can work to identify rare genetic disorders from a photo of a face, with 90 percent accuracy. Certain applications are being put in the hands of consumers, which is only possible because algorithms have become increasingly robust to diverse lighting conditions and other situations that arise as a result of less control over image capture.

  • Medical Imaging: Advances are now allowing for the automation of MRI evaluation, skin cancer detection, and a number of other important use cases.

  • Driver Assistance and Automation: Self-driving systems were failing when it was foggy, because they were unable to differentiate between heavy fog and a rock. Now, unsupervised learning and the ability to create synthetic data (led by the likes of Nvidia) are starting to be used to train the system on corner cases that even billions of recorded driving miles cannot uncover.

  • Agriculture: Companies like Blue River Technology (acquired by John Deere) are now reliably able to differentiate between weeds and crops, and selectively spray herbicide automatically, enabling a dramatic reduction in the quantity of toxic chemicals in use by commercial agriculture.

  • Real Estate and Property Information: Using computer vision on top of geospatial imagery could allow companies to automatically identify when floods, wildfires or hurricane-force winds may pose a danger to specific properties — allowing homeowners to take action faster, before disaster strikes.

When looking at these advances, one thing quickly becomes clear: Elon Musk wasn’t wrong. It’s just that his vision (robotic and otherwise) was a year or two away from reality. AI, computer vision and robotics are all nearing a tipping point of accuracy, reliability and efficacy. For Tesla, it means that the next ramp up to “production hell” (likely for the model Y) will see a vastly different assembly line at its Fremont and Shanghai factories — one that will more successfully implement robotics paired with computer vision.


Source: TechCrunch

The Silicon Valley exodus continues

For a long time, it was the norm for founders to haul their hardware to the 3000 block of Sand Hill Road, where the venture capitalists of “Silicon Valley” would be awaiting their pitches. Today, many of the investors that touted the exclusivity of “The Valley” have moved north to San Francisco, where they have better access to top entrepreneurs.

Y Combinator, a Silicon Valley institution and to many the lifeblood of the startups and venture capital ecosystem, is the latest to pack up shop. YC, which invests $150,000 for 7 percent equity in a few hundred startups per year, is currently searching for a space in SF to operate its accelerator program, sources close to YC confirm to TechCrunch, because the majority of YC’s employees and its portfolio founders reside in the city.

Founded in 2005, YC’s roots are in Mountain View, California. In its first four years, YC offered programs in Cambridge, Massachusetts and Mountain View before opting in 2009 to focus exclusively on The Valley. In late 2013, as more and more of its partners and portfolio companies were establishing themselves in SF, YC opened a satellite office in the city in what would be the beginning of its journey northbound.

The small satellite office, used to support SF-based staff and provide portfolio companies resources and workspace, is located in Union Square. The fate of YC’s Mountain View office is unclear.

YC’s move north will be the latest in a series of small changes that, together, point to a new era for the accelerator. Approaching its 15th birthday, YC announced in September it was changing up the way it invests. No longer would it seed startups with $120,000 for 7 percent equity, it would give startups an additional 30,000 to cover the expenses of getting a business off the ground and it would admit a whole lot more companies.

YC began mentoring its largest cohort of companies to date in late 2018. The astonishing 200-plus group in its winter 2019 batch is more than 50 percent larger than the 132-team cohort that graduated in spring 2018. To accommodate the truly gigantic group at YC Demo Days later this month (March 18 and 19), YC has moved to a new venue, SF’s Pier 48. Historically, YC Demo Days were hosted at the Computer History Museum near its home in Mountain View.

YC has also ditched “Investor Day,” which is typically an opportunity for investors to schedule meetings with startups that just completed the accelerator program. YC writes that the decision came “after analyzing its effectiveness.” On top of that, rumors suggest YC is planning to put an end to Demo Days. Other accelerators, AngelPad for example, put a stop to the tradition last year after realizing demo day was more of a stress to startup founders than a resource. Sources close to YC, however, tell TechCrunch these rumors are categorically false.

YC isn’t the first accelerator to ditch its Silicon Valley digs. 500 Startups, a smaller yet still prolific accelerator, opened an SF satellite office the same year as YC, and in 2018, the nine-year-old program made the decision to permanently relocate to SF. Venture capital firms, too, have realized the opportunities are larger in SF than on Sand Hill Road.

The transition from the peninsula to the city began around 2012, when VC heavyweights like Uber and Twitter-backer Benchmark opened an office in SF’s mid-market neighborhood. Months later, 47-year-old Kleiner Perkins, an investor in Stripe and DoorDash, opened the doors to its new workplace in SF’s South Park neighborhood.

Around that same time a whole bunch of firms followed suit: Shasta Ventures, Norwest Venture Partners, Accel, GV, General Catalyst and NEA opened SF shops, to name a few. Many of these firms, Benchmark, Kleiner and Accel, for example, held onto their Silicon Valley locations. Firms like True Ventures and Peter Thiel’s Founders Fund planted stakes in SF years prior. Both firms have operated SF offices since 2005; True Ventures, for its part, has managed a Palo Alto office from the get-go, as well.

“When we first started, it was [expected] that it would be maybe 60-40 Peninsula to the city; it’s actually turned out to be 80-20 SF to The Valley,” True Ventures co-founder Phil Black told TechCrunch. “For us, it was important to be near our customer: the founder. It’s important for us to be in and around where founders are doing their things.”

The transition out of The Valley is ongoing. Other VC funds are still in the process of opening their first SF offices as more partners beg for shorter commutes. Khosla Ventures, for example, is currently searching for an SF headquarters.

Silicon Valley real estate will likely remain a hot — or warm, at least — commodity, however. Why? Because long-time investors have lives established in that part of the bay, where they’ve built homes in well-kept, affluent cities like Woodside, Atherton and Los Altos.

Still, Y Combinator’s move highlights an increasingly adopted mantra: Silicon Valley isn’t the goldmine it used to be. For the best deals and greatest access to entrepreneurs, SF takes the cake — for now, that is. But with rising rents and a changing attitude toward geographically diverse founders, how long SF will remain the destination for top talent is an entirely different question.


Source: TechCrunch

Save big on popular Logitech gaming mice with Walmart’s latest sale

Have you been thinking of upgrading your gaming setup? You can currently save on popular Logitech gaming mice at Walmart, including the G900 Chaos Spectrum, the G600 MMO Gaming Mouse, and more. 

The post Save big on popular Logitech gaming mice with Walmart’s latest sale appeared first on Digital Trends.


Source: Digital trends

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