5G is coming! Now you can delete your games in peace!

  The 5G era is approaching. 5G is a communication technology that is known to be about 20 times faster than LTE (Long Term Evolution) that is currently used. So far, communication technology has evolved from 3G, the technology that connects people to people, to 4G, the world’s most wired technology, and now it’s 5G-the IoT(Internet of Things). In other words, the era of delivering larger capacity faster than before is coming soon.

 What gamers will experience by this benefit? It’s freedom from physical-although it doesn’t seem like physical, but you know, in general term-limits of ‘game files.’

 Think about this. Now, gamers install their games on their computer’s hard disk in order to play games. But-how about installing games on ‘Cloud server’? What about just ‘streaming’ game files from cloud server and play games without installing heavy and disk-consuming game files?

Surprisingly, Nintendo, a Japanese game company, already made this into reality. Because Nintendo company’s game don’t consume that much volume, cloud streaming service was able to be reality.

On May 21, Capcom introduced ” Resident Evil ” of cloud streaming version powered by Nintendo Switch. If you are connected to the cloud, without installing games, you can enjoy AAA-rated games. According to Capcom, to enjoy the ” Resident Evil ” cloud version, you only need to install 47 MB of data on the switch. It is really light-volume compared to the existing AAA-rated games have a minimum GB volume.

Some say that the Nintendo Switch loses its strength to play cloud-streaming games ‘ anytime, anywhere ‘. However, with data and graphics operations being processed by servers, it is likely that they will be able to produce the quality of game services beyond the hardware capabilities of existing Nintendo switches. Of course, a stable wireless data environment is essential.

Still, some users wonder, ” Wouldn’t cloud-streaming services catch an error? ” However 5G communication technology is expected to solve some of these doubts and concerns.

With 5G’s fast Internet speed, maybe a warning message saying that “There is a lack of storage space” can be fossil being.

[Column scrap : Can we teach morality to machines? Three perspectives on ethics for artificial intelligence]

Original Link : https://medium.com/@drpolonski/can-we-teach-morality-to-machines-three-perspectives-on-ethics-for-artificial-intelligence-64fe479e25d3

Before giving machines a sense of morality, humans have to first define morality in a way computers can process. A difficult but not impossible task.

Today, it is difficult to imagine a technology that is as enthralling and terrifying as machine learning. While media coverage and research papers consistently tout the potential of machine learning to become the biggest driver of positive change in business and society, the lingering question on everyone’s mind is: “Well, what if it all goes terribly wrong?”

For years, experts have warned against the unanticipated effects of general artificial intelligence (AI) on society. Ray Kurzweil predicts that by 2029 intelligent machines will be able to outsmart human beings. Stephen Hawking argues that “once humans develop full AI, it will take off on its own and redesign itself at an ever-increasing rate”. Elon Musk warns that AI may constitute a “fundamental risk to the existence of human civilization”. Alarmist views on the terrifying potential of general AI abound in the media.

More often than not, these dystopian prophecies have been met with calls for a more ethical implementation of AI systems; that somehow engineers should imbue autonomous systems with a sense of ethics. According to some AI experts, we can teach our future robot overlords to tell right from wrong, akin to a “Good Samaritan AI” that will always act justly on its own and help humans in distress.

Although this future is still decades away, today there is much uncertainty as to how, if at all, we will reach this level of general machine intelligence. But what is more crucial, at the moment, is that even the narrow AI applicationsthat exist today require our urgent attention in the ways in which they are making moral decisions in practical day-to-day situations. For example, this is relevant when algorithms make decisions about who gets access to loans or when self-driving cars have to calculate the value of a human life in hazardous traffic situations.

Moral dilemmas for self-driving cars (Source: MIT Media Lab)

Moral problems in everyday life

Teaching morality to machines is hard because humans can’t objectively convey morality in measurable metrics that make it easy for a computer to process. In fact, it is even questionable whether we, as humans have a sound understanding of morality at all that we can all agree on. In moral dilemmas, humans tend to rely on gut feeling instead of elaborate cost-benefit calculations. Machines, on the other hand, need explicit and objective metrics that can be clearly measured and optimized.

For example, an AI player can excel in games with clear rules and boundaries by learning how to optimize the score through repeated playthroughs. After its experiments with deep reinforcement learning on Atari video games, Alphabet’s DeepMind was able to beat the best human players of Go. Meanwhile, OpenAI amassed “lifetimes” of experiences to beat the best human players at the Valve Dota 2 tournament, one of the most popular e-sports competitions globally.

But in real-life situations, optimization problems are vastly more complex. For example, how do you teach a machine to algorithmically maximise fairness or to overcome racial and gender biases in its training data? A machine cannot be taught what is fair unless the engineers designing the AI system have a precise conception of what fairness is.

This has led some authors to worry that a naive application of algorithms to everyday problems could amplify structural discrimination and reproduce biases in the data they are based on. In the worst case, algorithms could deny services to minorities, impede people’s employment opportunities or get the wrong political candidate elected. Some people have argued that the use of AI in politics already had disastrous consequences.

Thinking about new ways to teach robots right from wrong.

So what can we do about it? Based on our experiences in machine learning, we believe there are three ways to begin designing more ethically aligned machines with the following guidelines:

1. Explicitly defining ethical behaviour

AI researchers and ethicists need to formulate ethical values as quantifiable parameters. In other words, they need to provide machines with explicit answers and decision rules to any potential ethical dilemmas it might encounter. This would require that humans agree among themselves on the most ethical course of action in any given situation — a challenging but not impossible task. For example, Germany’s Ethics Commission on Automated and Connected Driving has recommended to specifically programme ethical values into self-driving cars to prioritize the protection of human life above all else. In the event of an unavoidable accident, the car should be “prohibited to offset victims against one another”. In other words, a car shouldn’t be able to choose whether to kill one person based on individual features, such as age, gender or physical/mental constitution when a crash is inescapable.

2. Crowdsourcing human morality

Engineers need to collect enough data on explicit ethical measures to appropriately train AI algorithms. Even after we have defined specific metrics for our ethical values, an AI system might still struggle to pick it up if there is not enough unbiased data to train the models. Getting appropriate data is challenging, because ethical norms cannot be always clearly standardized. Different situations require different ethical approaches, and in some situations there may not be a single ethical course of action at all — just think about lethal autonomous weapons that are currently being developed for military applications. One way of solving this would be to crowdsource potential solutions to moral dilemmas from millions of humans. For instance, MIT’s Moral Machine project shows how crowdsourced data can be used to effectively train machines to make better moral decisions in the context of self-driving cars.

3. Making AI systems more transparent

Policymakers need to implement guidelines that make AI decisions with respect to ethics more transparent, especially with regard to ethical metrics and outcomes. If AI systems make mistakes or have undesired consequences, we cannot accept “the algorithm did it” as an adequate excuse. But we also know that demanding full algorithmic transparency is technically untenable (and, quite frankly, not very useful). Neural networks are simply too complexto be scrutinized by human inspectors. Instead, there should be more transparency on how engineers quantified ethical values before programming them, as well as the outcomes that the AI has produced as a result of these choices. For self-driving cars, for instance, this could imply that detailed logs of all automated decisions are kept at all times to ensure their ethical accountability.

How can moral values be measured and optimised?

Next steps for moral machines

We believe that these three recommendations should be seen as a starting point for developing ethically aligned AI systems. Failing to imbue ethics into AI systems, we may be placing ourselves in the dangerous situation of allowing algorithms to decide what’s best for us. For example, in an unavoidable accident situation, self-driving cars will need to make some decision for better or worse. But if the car’s designers fail to specify a set of ethical values that could act as decision guides, the AI system may come up with a solution that causes more harm.

This means that we cannot simply refuse to quantify our values. By walking away from this critical ethical discussion, we are making an implicit moral choice. And as machine intelligence becomes increasingly pervasive in society, the price of inaction could be enormous — it could negatively affect the lives of billions of people.

Machines cannot be assumed to be inherently capable of behaving morally. Humans must teach them what morality is, how it can be measured and optimised. For AI engineers, this may seem like a daunting task. After all, defining moral values is a challenge mankind has struggled with throughout its history. If we can’t agree on what makes a moral human, how can we design moral robots?

Nevertheless, the state of AI research and its applications in society require us to finally define morality and to quantify it in explicit terms. This is a difficult but not impossible task. Engineers cannot build a “Good Samaritan AI”, as long as they lack a formula for the Good Samaritan human.

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About the authors: Jane Zavalishina is the CEO of Yandex Data Factory, a provider of AI-based solutions for industrial companies. Jane is a frequent speaker on the topics of AI business strategy and applications at various events in Europe, Middle East and Asia. She serves on the World Economic Forum’s Global Future Councils. In 2016, Jane was named in Silicon Republic’s Top 40 Women in Tech as an Inspiring Leader and recognised by Inspiring Fifty as one of the top 50 most inspirational women in the technology sector in the Netherlands.

Dr Vyacheslav Polonski is a researcher at the University of Oxford, studying complex social networks and collective behaviour. He holds a PhD in computational social science and has previously studied at Harvard, Oxford and LSE. He is the founder and CEO of Avantgarde Analytics, a machine learning startup that harnesses AI and behavioural psychology for the next generation of algorithmic campaigns. Vyacheslav is actively involved in the World Economic Forum Expert Network and the WEF Global Shapers community, where he served as the Curator of the Oxford Hub. He writes about the intersection of sociology, network science and technology.

Earlier versions of this article were published on the Net Politics Blog of the Council on Foreign Relations on 14 November 2017, the World Economic Forum Agenda on 23 November 2017 and the official blog of the BCG Centre for Public Impact on 12 December 2017. The article was also translated into French and Polish in other online media outlets.

[Column Scrap]Samsung Galaxy S9+ tested: Exynos 9810 vs. Snapdragon 845

For original post : https://www.androidcentral.com/samsung-galaxy-s9-tested-exynos-9810-vs-snapdragon-845

 

From the Galaxy S7 onward, Samsung has offered two variants of its flagships — a model powered by Qualcomm’s Snapdragon platform for the U.S. and China, and a global variant featuring the manufacturer’s in-house Exynos chipset. That’s the case this year as well: the Galaxy S9 and S9+ sold in the U.S. and China are powered by Qualcomm’s latest Snapdragon 845, while the models sold in South Korea, UK, India, and other global markets are running the Exynos 9810.

Traditionally, there haven’t been a lot of differences between the two variants — the Exynos 8895-powered Galaxy S8+ had marginally better battery life over its Snapdragon sibling, but there wasn’t a performance differential. With my unit powered by the Exynos 9810, and my colleague Andrew Martonik using the Snapdragon variant, it’s time to see how the two variants differ.

Hardware

https://tpc.googlesyndication.com/safeframe/1-0-17/html/container.html

There isn’t a whole lot to separate the Exynos 9810 from the Snapdragon 845 when it comes to the manufacturing side of things, as both chipsets are built on Samsung Foundry’s second-gen 10nm node. The Exynos 9810 features Samsung’s third-generation custom core, the M3, along with a Mali-G72 MP18 GPU. The Snapdragon 845, meanwhile, sees the introduction of the Kryo 385 cores, along with the Adreno 630 GPU.

As is to be expected, both chipsets offer increased performance, with Samsung touting a 2x increase in single-core performance compared to last year’s Exynos 8895, as well as a 40% uptick in multi-core performance. That’s largely due to the M3 cores, which are now clocked at 2.90GHz, a significant bump from the 2.3GHz M2 cores last year. The configuration of the cores itself hasn’t changed — there are four M3 high-performance cores that go up to 2.90GHz, backed by four energy-efficient Cortex A55 cores at 1.90GHz.

The Exynos 9810 nearly doubles CPU performance from last year.

The second set of cores are also significant, as they’re the long-awaited sequel to the Cortex A53. The A53 has been a mainstay on budget phones and flagships alike for a few years now, and the A55 delivers more performance while consuming less power.

The uptick isn’t as radical when it comes to the GPU side of things. Like its predecessor, the Mali-G72 MP18 is based on ARM’s Bifrost architecture, and introduces several tweaks and optimizations to eke out more performance. This year’s GPU has 18 cores (hence the MP18 denomination) whereas the one seen in the Galaxy S8+ last year, the Mali-G71 MP20, had 20 cores. The core frequency hasn’t seen a drastic uptick either — the G72 is clocked at 572MHz, just 30MHz more than the G71. As a result, the overall graphics performance is marginally better than last year.

With the Snapdragon 845, Qualcomm is also sticking to an octa-core layout, with four high-performance cores joined by four energy-efficient cores. This year we have the Kryo 385 core, which is a semi-custom design that’s based on two of ARM’s cores.

The heavy lifting is done by the 2.80GHz core based on the Cortex A75 — ARM’s latest offering — and the energy-efficient tasks are handled by a 1.77GHz core based on the Cortex A55. As for the GPU, Qualcomm is touting a 30% uptick in performance from the Adreno 630 over last year’s Adreno 540.

Benchmarks

Synthetic benchmarks give us a high-level overview of where the two chipsets differ. Based on the hardware, the Exynos 9810’s M3 CPU should outperform the Snapdragon 845, whereas the Adreno 630 should have no issues pulling out ahead of the Mali-G72. Let’s see if that’s indeed the case. I’ve also included scores from the Exynos 8895-powered Galaxy S9+ to see how things have changed over the course of a year.

AnTuTuGeekbenchBasemarkGFXBench3DMarkChrome

AnTuTu Benchmark v7.0.4

Device	Overall score
Galaxy S9+ (Snapdragon 845)	263494
Galaxy S9+ (Exynos 9810)	235913
Galaxy S8+ (Exynos 8895)	184806

AnTuTu Benchmark v7.0.4

Device	CPU	GPU	UX	Memory
Galaxy S9+ (Snapdragon 845)	88377	107305	58657	9155
Galaxy S9+ (Exynos 9810)	81915	94881	50681	8436
Galaxy S8+ (Exynos 8895)	58957	77255	44139	4455

AnTuTu isn’t a reliable indicator of how good a device is to use on a day-to-day basis, but it maintains a leaderboard that gives us an idea of where a particular phone ranks in the overall scheme of things.

As you can make out from the scores above, the Adreno 630 is some way ahead of the Mali G72, breaking the 100,000 mark on AnTuTu.

Which is better?

Hardware is just one part of the story with these two chipsets, as the way the software is set up makes a lot of difference as well. With Samsung Experience 9.0 atop Android 8.0 Oreo, the overall experience is similar between both devices.

I haven’t faced any slowdowns on my Exynos Galaxy S9+ even while playing visually-intensive games, and in his review, Andrew said that the Snapdragon 845 is “far more powerful than anything we need in a smartphone today:”

The Galaxy S9+ handled everything I threw at it without any hesitation, and I experienced zero slowdowns, app crashes or system instability. The phone’s been rock solid, and I just hope it stays that way over time.

Regardless of whatever model you end up with, you’re getting a phone with top-notch performance.

Getting Both Safety And Economic Efficiency By Blocking Standby Power

Several weeks ago, I compete in 2018 EngineerGirl Writing Contest-Engineering for Your Community

Yee

It’s about solving engineering problem in our daliy life, and my idea is SOOOOOOOOOO fine that I want to post on my blog.

So, yeah. Have fun.

Introduction

Standby power is an electric power used to maintain a ready-state of electric appliances when electric appliances are turned off. The average standby power is 10% of the normal volume of electricity usage of a machine. By using standby power, electric appliances can immediately response to a user’s command even though electric appliances’ power is turned off.

The concept of standby power can be explained easily when we compare the concept with turning on a television. It needs only a moment to turn on a plugged television when we grab a remote and press a button, and the television immediately responds to the signal sent by remote. On the other hand, when we turn on an unplugged television, it needs time for the television to catch a signal from the remote and respond to that signal. It is because when the television does not have sufficient standby power to operate its sensor to receive signal from the remote, the television cannot respond to a user’s order.

 

Identifying Problems

It seems like standby power is necessary for users of electric appliances, but actually, it is not always necessary. In fact, it can not only cause more electric charges, but it also threatens our safety because of following reasons.

First, standby power is consumed no matter a machine is turned on or not. Maybe some people would think standby power is not big enough to consider, but it is. The average standby power is 10% of the normal volume of electricity usage of a machine. So, if we calculate a little, a 10 days of plugged and turned off machine consumes a same amount of electric power of a 1 day constantly turned on machine.

Second, standby power can cause overflowing of electricity. Because all the electric appliances are connected parallely (it is because every electric appliance needs same volume of voltage), connecting more electric appliances causes resistance to decrease and makes electrical current increases. Increasing of electrical current means electric appliances get bigger electrical current. It can cause short circuit because electric wires cannot endure overflowing electrical  current. As a result, electric wires melt down and stick together and easily cause fire.

Possible Solutions

Due to these problems, it is necessary to block standby power and prevent these problems. So, what can be done to block standby power? Users can just unplug machines when they do not use machines, but they usually get tired and give up. Also, if electric devices are unplugged, devices need more time to response to the users’ commend. Because of these problems, I researched possible solutions.

First, I found a plug named “smart plug,” which is controlled by a smartphone. It resembles a normal multi-outlet. To control this plug, a user must connect the plug to the Internet and install the certain application. The smart plug has many advantages. First, the smart plug can cut out an electricity without unplugging electric appliances. The smart plug can remember the amount of an electric appliance’s standby power and cut out electricity when the electric appliance consumes standby power. Second, it gives the user helpful information. For example, the user can see the electricity that the user is currently using, the time the user used electricity, and the estimated electricity bill that is automatically calculated based on that data by the application. In addition, the application’s interface is intuitive and easy to view.

But in close observation, this has also disadvantages. First, it is expensive. It costs about thirty dollars to fourty dollars per one smart plug. For one ordinary house, to cut out every standby power, at least 5 or 6 plugs are needed-for a freezer, a laundry machine, a dryer, a television, and an air conditioner,-which means the house should spend over one hundred dollars. Second, it cannot solve the problem of cutting off standby power. As mentioned before, if the user cuts off standby power, the electric appliance needs more time to react to the user’s command. Third, a smart plug also consumes electricity. The smart plug is not the best solution for controlling standby power because of these reasons.

For the second solution, I found some electric devices with a button called “standby power cut-out switch.” It is built in the newest electronic devices. The function of standby power cut-out switch is, literally, cutting off the standby power. It does not need any extra fee to pay and is relatively easier than unplugging electric appliances. However, it is built in only the newest electric appliances and still has a problem of delation of reacting to the user’s command. All reasons considered, standby power cut-out switch is not the the best solution for controlling standby power either.

 

Engineering Design

Then what may be the best solution for the standby power? I have considered the way to cease electric consumption and make use of standby power. I could not abandon the advantage of a quick reaction of electric appliances, so cutting out standby power from machines was not considered as a solution, but consuming electricity from an electrical outlet causes problems. I needed another power source of standby power. I think that attaching an additional battery can solve the problem, rather than using electricity from an electrical outlet, which is attached to a wall.

I named this device as “a capacitor plug.” The capacitor plug consists of a battery and a switch that can change a circuit.

The way how it operates is simple. When a user uses a machine, a capacitor plug, which is attached between the electrical outlet and the machine, consumes additional electricity to charge itself. After the capacitor plug is fully charged, it stops consuming electricity. The capacitor plug can hold up to 24 hours of standby power.

After the electric appliance is turned off, the capacitor plug starts to consume its battery. During this process, the electric appliance is cut off from the electrical outlet, so overflowing of electricity cannot be occured. Also, the electric appliance still receives standby power, so the electric appliance can quickly response to a user’s command.

For the machines, such as an air conditioner or a radiator that are usually used for only a short term, capacitor plugs automatically disconnect the machine after the batteries inside the capacitor plugs are empty. Thus, the capacitor plugs can cut off standby power of the machines if the machines are not used for a long period. If a user wants to use the electric appliance after the capacitor plug disconnects the electric appliance, what the user should do is simply pressing the switch to change the circuit and let electricity flow to the electric appliance.

The capacitor plug can take advantage of standby power and mitigate the drawbacks of standby power. Therefore, I believe this design may be the best solution for the problem. Still, this does not mean that my solution is perfect.

The problem is that the battery that can hold up to 24 hours of standby power does not exist because of the lack of technology. The battery that can contain more than 24 hours of standby power does exist, however. It is a car battery. As everyone knows, a car battery is too big and expensive to attach to the capacitor plug. It is inefficient. To solve this problem, I thought that a superconductor should become commercially available. The superconductor is a material that makes electric resistance extremely small. Due to the extremely small electric resistance, the battery can contain more electricity and supply standby power more efficiently.

 

Conclusion

Standby power is an issue that worth our deliberation. It causes many dangerous situations (such as short circuit), waste of energy, and environmental pollution. Followings are the possible solutions to solve standby power issues; first, use a smart plug; second, use a standby power cut-off switch; third, use a capacitor plug, which is my design. Among these solutions, a capacitor plug can reduce a hazardness of standby power and utilize standby power’s advantage. Therefore, a capacitor plug is the most efficient solution that would give people environmental and financial benefits.