It’s still hard for me to understand Tesla’s market cap, currently $35B ($35,000,000,000). Sales of 11K units/quarter are still smaller than tiny (for an auto company). Of course if it achieves a 50% annual growth trajectory for 5 years, that would justify a huge value – but I have not read anything saying that they are able to scale manucturing at that rate. (Admission: I used to consistently say that Apple was over-valued. And I was consistently wrong.)
“Tesla said in a filing Thursday that it delivered 11,507 vehicles during the April-through-June period, a 52 percent increase compared with the same quarter a year ago.”
I also did not realize that Musk was not a founder of the company. (1) Breakthrough Strategy and Innovation.
Something I just found for my Big Data class.
Machine learning system aims to remove problem players “within 15 minutes.”
An interesting thread of player comments has a good discussion of potential problems with automated bans. Only time will tell how well the company develops the system to get around these issues.
This company also took an experimental approach to banning players. And hired 3 PhDs in Cognitive Science to develop it. (Just to be clear, their experiments did not appear to be automated A/B style experiments.) After the jump is a screen shot from that system.
But, I’m not tempted to play League of Legends to study player behavior and experiment with getting banned! (I don’t think I’ve ever tried an MMO beyond some prototypes 15 years ago.) If any players want to post your observations here, great.
A bad graphic from a pro-solar group is perhaps not surprising. (See previous post.) Here is one from Bloomberg that verges on incomprehensible. Bloomberg as a source is surprising.
Which way is up? (Answer: down is up)
Looking closer, it appears that Skill Desirability increase from left to right, and Skill Frequency increases from top to bottom?! Graphs should be drawn so that UP means higher. In any case, it should not take prolonged inspection to deduce which variable is on the X axis.
The graphic also manages to make as many schools as possible look good at something. In Financial Services, the top 3 schools for Communications skills are listed as Tuck, McCombs, and Kellogg. But in Technology, the top 3 schools change to Fuqua, Haas, and Kellogg. And for Consulting, the top 3 are London, Harvard, and Ivey. Since “Communication Skills” are the most desired skill of all according to the graph, eight schools can say they are in the Top 3 for teaching the most sought-after skills.
Distorted pictures. The water droplets are drawn with linear scaling, when they should use area scaling. 672 gallons is about 3X 198 gallons, but the picture looks 11X larger!
Selective facts. Once-through nuclear cooling is about 400 gallons/MWh. Solar thermal normally uses wet cooling, with up to 900 gallons/MWh, or “500 to 800 gal/MWh.” (US DOE) New solar thermal dry cooling tech can reduce this “90%”, but does not work well on hot days. And dry cooling is also possible for nuclear plants.
SO distorted. Both visually and in substance.
There are plenty of arguments pro and con various energy technologies, but blatant distortion does not help make good decisions!
I recently audited some lectures by friend and China expert Prof. Susan Shirk. She bans computers in her lectures. But one student sitting near me had his machine out and was “busy” with the usual distractions. (Didn’t he know the Associate Dean was a few seats away?) I asked Susan about him after class. “He told me he can’t take notes without a computer.” Obviously the computer is not the big issue on his note taking. Actually, it probably IS the issue – but in a negative way.
Not one computer mirrors the overheads.
James Kwak has beaten the distraction of cell phones – by removing most apps, including browsers.
I know that its enormous powers of distraction also make me lose focus on work, tune out in meetings, stay up too late at night, and, worst of all, ignore people in the same room with me. We all know this. We’re addicted to the dopamine hit we get when we look at our email and there’s actually something good in there, so we keep checking our email hoping to feel it again.
via How I Achieved Peace by Crippling My Phone — Bull Market — Medium.
Clay Shirky, an Internet sociologist, has a good discussion of why he recently banned computers in his classrooms. Excerpt:
I came late and reluctantly to this decision — I have been teaching classes about the internet since 1998, and I’ve generally had a laissez-faire attitude towards technology use in the classroom. This was partly because the subject of my classes made technology use feel organic, …. And finally, there’s not wanting to infantilize my students, who are adults, even if young ones — time management is their job, not mine.
Despite these rationales, the practical effects of my decision to allow technology use in class grew worse over time. The level of distraction in my classes seemed to grow, even though it was the same professor and largely the same set of topics, …
Over the years, I’ve noticed that when I do have a specific reason to ask everyone to set aside their devices (‘Lids down’, in the parlance of my department), it’s as if someone has let fresh air into the room. The conversation brightens, and more recently, there is a sense of relief from many of the students. Multi-tasking is cognitively exhausting — when we do it by choice, being asked to stop can come as a welcome change.
So this year, I moved from recommending setting aside laptops and phones to requiring it, adding this to the class rules: “Stay focused. (No devices in class, unless the assignment requires it.)” …
The rules for flying radio controlled aircraft are under tremendous debate and change, mainly because of two new technologies that have together created a new business. The technologies are tiny flight management systems costing about $100, and excellent lightweight cameras like the GoPro (invented by a UCSD grad). The new business is using drones for low-altitude photography (and eventually for other applications, although IMO not for package delivery).
Congress put the Federal Aviation Administration in charge of figuring out what rule changes are needed. So far it has done a slow and weak job. (One result is that the U.S. has lost leadership of the industry, and may even become a backwater. That is a topic for another day.)
Pilots are instinctively concerned about risks to manned aircraft, from unmanned aircraft. Much argument back and forth has ensued, but there is little or no modeling or investigation. (What happens when a 2 pound quadcopter collides with small plane at 140 knots? Apparently there have been zero experiments on the issue.) Here is an interesting blog post on this issue.
Why See and Avoid Doesn’t Work – AVweb Insider Article.
My take on this issue is that the likelihood of serious air-to-air collisions is tiny. Far fewer than bird strikes, for example. A much bigger sour of injuries will be untrained idiots flying drones over crowds of people.
This short interview has some good explanations.
LeCun: Actually, I think the basics of machine learning are quite simple to understand….
A pattern recognition system is like a black box with a camera at one end, a green light and a red light on top, and a whole bunch of knobs on the front. The learning algorithm tries to adjust the knobs so that when, say, a dog is in front of the camera, the red light turns on, and when a car is put in front of the camera, the green light turns on. You show a dog to the machine. If the red light is bright, don’t do anything. If it’s dim, tweak the knobs so that the light gets brighter. If the green light turns on, tweak the knobs so that it gets dimmer. Then show a car, and tweak the knobs so that the red light get dimmer and the green light gets brighter. If you show many examples of the cars and dogs, and you keep adjusting the knobs just a little bit each time, eventually the machine will get the right answer every time.
Why unsupervised learning is critical in the long run, but does not yet work:
The type of learning that we use in actual Deep Learning systems is very restricted. What works in practice in Deep Learning is “supervised” learning. You show a picture to the system, and you tell it it’s a car, and it adjusts its parameters to say “car” next time around. Then you show it a chair. Then a person. And after a few million examples, and after several days or weeks of computing time, depending on the size of the system, it figures it out.
Now, humans and animals don’t learn this way. You’re not told the name of every object you look at when you’re a baby. And yet the notion of objects, the notion that the world is three-dimensional, the notion that when I put an object behind another one, the object is still there—you actually learn those. You’re not born with these concepts; you learn them. We call that type of learning “unsupervised” learning.
Facebook AI Director Yann LeCun on His Quest to Unleash Deep Learning and Make Machines Smarter – IEEE Spectrum.