Getting started with Artificial Intelligence can lead to numerous questions and confusion, given the speed the world is changing and adopting this technology. There are plenty of resources available online but there has to be a start point.
In this article, there is a brief introduction of Artificial Intelligence covering all its important aspects which one must go through to get a clear picture of this emerging technology. Artificial Intelligence has made commendable progress and is developing at a lightning-fast speed covering every industry of a market.
AI has become a necessity rather than an extra activity to know about this technology and its evolving faces.
The Basics Of Artificial Intelligence
Artificial Intelligence can be understood as a simulation of human intelligence. A simulation of human intelligence means that any task performed by a program or a machine will be carried out in the same way in which a human being would have done it.
Artificial intelligence cannot be given a single line definition. It has an ultra-wide scope to deal with problems and to learn through past experiences. The core part of artificial intelligence is the algorithms.
AI demonstrates some of the behavior that is linked with human intelligence such as planning, reasoning, learning, manipulation, creativity, and more.
The concept of what AI is and what it can do has changed from time to time. But the core idea can be explained as machines that can think and act like humans.
Developers and researchers are constantly working towards making the machines capable of interpreting the world around and picking up change whenever required.
These are some of the basic characteristics of a human being and machines are being taught extensively for a decade through algorithms and programs.
Different Types Of AI
There are many sub-parts of Artificial Intelligence but on a high level, it can be broadly divided into two types - narrow AI and general AI.
Narrow AI can be seen in all the computing devices that people use in their day-to-day lives. That is why they know how they need to execute some of the functions on themselves. This is what makes our lives easier.
People just need to press some buttons and all other work will be done by the machine. For example, we can take the voice assistants that make smartphones way smarter than they were before.
Earlier people needed to do everything by themselves but now they can ask the voice assistant to do those things for them. They can ask it for the temperature, the time, to call a contact, to read out the messages, and a lot more. These assistants are improving even more as AI is improving.
A few years ago no one would have believed that something like AI would change the way technology behaved so much. And now the development industry is looking at it like they have never looked at anything in the past.
The AI tech is the technology that has the power to change the way humans and machines interacted. There can even be a time when people will not need to touch their machines for tasks like calling and writing emails.
The AI will teach the assistants and the applications about all these things. This is something that will help enterprises the most. This will help them to increase their productivity and will save a lot of their time.
The need to hire someone just to do their work will also be eliminated and that will reduce the expenses. There are many benefits of AI for both the general public and enterprises.
What Can Narrow AI Do?
This type of AI can help the traffic and surveillance departments by interpreting the video feeds that a drone or a CCTV camera takes. It can store information, categorize it, and give numbered reports to improve the services and to ensure the safety of an enterprise or a city.
The reason why governments want AI to be developed properly as soon as possible is that they will also get a lot of benefits. AI along with technologies like IoT can make cities smart and the administration smarter.
There are many applications of AI that can help authorities to analyze the current situations and make a better plan for the future. These things can be done with great quality with AI.
It can also organize and remind business people about their events and meetings. It can schedule emails, and make content personalized for better marketing and engagement with consumers.
These are just a few of the things that can be done by narrow AI, this is one of the things that have already made the applications and the devices that people use every day very smart.
Businesses don’t need to pay and use very high-end software because their smartphones have many advanced features in them. In enterprise development too narrow AI is doing a really great job.
Developers are trying to teach their application most of the things that can save the time of the users. AI makes social media, search engines, and websites smart.
What Is General AI And What Can It Do?
Now, this is the best form of AI as it teaches machines or software to do the things that human beings can do. This is the reason why it is called Artificial General Intelligence, which means machines that can work like people.
They can be taught how they can make reports or polish shoes or iron clothes. These are things that can help them to do specific tasks just like humans.
They might do them in an even better way than humans with more accuracy and speed. General AI developers are focusing on developing machines and software that reduce the efforts that people have to put into things that can be easily done by a machine.
This will not eat up jobs, instead, it will increase productivity to a great extent. This will make the world truly smart and intelligent.
This is about making machines that can understand things by themselves and then carry out the tasks for which they have been created.
They can be studying data or the environment and then according to the things that they can do, they can make decisions. This is intelligent and this is an artificial intelligence future technology.
ML or machine learning is what makes an AI application intelligent enough to learn new things. This is the broad part of AI that most industries in the world are working on.
Because of this, a computer device takes up data and with more data, it gets more intelligent. It is like feeding a human being with food that makes them stronger. Data makes a device that has used ML smart and intelligent.
Because of this, the software can learn how they need to improve and personalize themselves according to their users. ML is also used in the voice assistants because of which they can remember what wesay and search related to that.
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Elements Of Machine Learning
Machine learning can be regarded as a subset of AI and has mainly two elements, namely supervised learning and unsupervised learning.
This is a rather common technique for teaching systems. It is done by using a huge number of labeled examples in the form of data. The systems are filled with a large amount of data that identifies the features of interest.
These are then labeled into the system’s memory. Once the system is trained, these labels can be used to read data as well as create new data.
It is quite different from the above method of learning as this algorithm attempts to locate patterns in data. They cluster together these patterns to perform operations and give meaningful results.
This is a reward-based learning process. Here, rewards are processed according to their input data. This is basically a trial and error process and is greatly used in machine learning methods.
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AI Changing The World
AI will change the world in many different ways. There are different parts of industries that will be directly affected by it like robotics and the automobile sector.
AI will make robots and cars smart enough to be able to work on their own and control the things around them as per their programming. Though it will take some time to make them behave as normal as humans, even as of now they will be able to have a conversation, understand what people say, and respond to them.
Other parts that will be affected by it is the content that gets uploaded on the internet. Search engines have started using AI to filter out information and news that is fake.
This is the reason why now the internet does not list any website that is caught spreading fake news or information about something. This will make the internet a better place for normal people. These are just some of the ways, there are many other ways in which it will change the world.
Artificial Intelligence is the future. It has the scope to change the technology for the betterment of the world and the people living in it.
AI will help businesses to improve their processes, the government to improve the administration, and the general public to do daily work in an easy way.
How Do Delivery Robots Work? How They Safely Deliver Your Packages
A distant future involving robotic package deliveries is now very much a reality. Advances in robotics, GPS tracking, automation, and navigation now mean you might not find a delivery person at your door with your package.
You might find a delivery robot instead.
With semi-autonomous robots beginning to enter the world, here’s a look at how delivery robots work.
What Is a Delivery Robot?
A delivery robot is an automated robot that brings your delivery directly to your door. These robots aren’t walking and talking humanoids; rather, these robots are cute delivery containers on six wheels, resembling giant (but friendly-looking!) bugs.
As with other delivery services, you make your purchases through an app with vendors based on your location. The robot trundles to the vendor—whether for shopping, food, drinks, or otherwise—and then it makes its way to your home.
How Does a Delivery Robot Work?
The primary example of delivery robots in action comes from Starship Technologies, a company based out of San Francisco with engineering facilities in Estonia and Finland. Starship Technologies is the brainchild of Skype co-founders Janus Friis and Anti Heinla, and they are currently the largest “last mile” delivery robot company around.
So, how does an autonomous delivery robot make a delivery?
The robots have a cargo capacity of around 9kg, can travel at a maximum speed of 4 mph, weigh around 25kg, and cost over $5,000 to manufacture. The delivery robot uses many of the same features as an autonomous car: 10 cameras for 360-degree vision, several ultrasonic sensors, GPS navigation, measurement units, gyroscopes, and much more.
How Do Delivery Robots Navigate?
The route between a vendor and a delivery point might look A-to-B if you plug the locations into a navigation app… but there are extra considerations for a delivery robot, including sidewalks, crossings, driveways, humans, animals, vehicles, and so on.
Starship’s robots calculate a route based upon the shortest distance and satellite imagery detailing the route. Each feature on the route (crossings, driveways, etc.) receives a time calculation, which the robot factors into route selection and delivery time.
Over time, the robots build a collaborative memory of an area, creating a wireframe map of constant features (buildings, crossings, statues, pathways, etc.) and ensuring that future journeys through the area are faster. The collaborative area-building makes navigation easier for every robot in the vicinity, with all units contributing to building out the local map.
But navigation isn’t always smooth sailing. Aside from regular navigational dilemmas, a malfunctioning robot comes with its own problems. For example, a Starship robot in Milton Keynes malfunctioned—and drove straight into a canal.
Does Anyone Control the Delivery Robot?
While the Starship Technology robots are autonomous, they are not disconnected from their operators. If a robot comes up against a significant challenge, such as a particularly massive curb (they can climb up and over regular sidewalk curbs), a human operator can take control and find a solution.
But for the most part, the robots are designed to take everything into account, focusing strongly on the sidewalk. Delivery robots sharing the same routes as pedestrians has all the potential for irritation.
All these potential issues are all considered, but the robots must learn the correct way to interact with humans. How many times have you faced the awkward situation of walking at a similar pace to someone just ahead of you? Do you speed up to pass, then continue walking faster? Do you slow down to give them time to move further ahead? Is your destination close enough so that you don’t need to overtake?
The delivery robots are learning how to solve these problems, as well as countless others.
If you want to get involved with robotics, check out these DIY robotic arm kits.
How Do You Order Take-Out From a Robot?
Starship’s robotic delivery team are currently operating in several US cities but in limited geographic areas. For example, you can order via Starship at Arizona State University, in Fairfax City, Virginia, or Modesto, California—but only in a limited area. The images below show the delivery areas for those respective locations:
If the vendor you want to order from and your delivery address are with the bounds of the robot, you can order from the Starship Delivery app. The app displays a list of vendors you can make an order with. You place your order, and a local delivery robot makes its way to the vendor to pick up your order. The robot then trundles to your front door. You track the delivery robot using an app, as well as unlock the secure cargo compartment, too.
The Starship Technologies delivery service costs $1.99 per delivery.
For vendors, the reality is slightly different. The delivery robots are cute and get the job done, but Starship’s terms of partnership can take up to a 20% cut per delivery, after a free month’s trial of the service.
Delivery Robots and COVID-19
The 2020 COVID-19 pandemic provided a new and interesting dynamic for Starship Technologies and its delivery robots. With huge numbers of people entering lockdown at differing times and with many people attempting to self-isolate and socially distance from the general public, the delivery robots present a perfect non-human delivery system.
In Milton Keynes, UK, the demand for robot deliveries rose significantly during the early stages of the UK COVID-19 lockdown. The US cities and university campuses also saw similar demand for robotic, almost zero-human interaction deliveries. For those on at-risk lists due to pre-existing conditions or healthcare workers struggling to purchase groceries after a long shift, robotic deliveries are a vital lifeline.
Does Amazon Have Delivery Robots?
Starship Technologies was the first company to use delivery robots as its core delivery method. Recognizing that last-mile delivery is a phenomenally large market is a masterstroke. But the world’s largest online marketplace, Amazon, isn’t far behind.
Amazon Scout is another six-wheeled robot that moves across sidewalks and crossings at walking pace, but this one brings your Amazon delivery directly to your door. Scout is currently available to Amazon customers in the area near Amazon’s headquarters in Seattle, as well as Irving, California, with recent trial expansions to Atlanta, Georgia and Franklin, Tennessee.
Delivery Robots Are Coming to Your Home
A friendly delivery robot bringing curry to your door is charming and is a reality for millions of people. The rollout of delivery robots won’t be overnight, and there are significant challenges for the delivery robotics sector, as well as rural communities.
If you like the sound of robots, check out these robots that’ll do your chores!
Image Credit: JHVEPhoto/Shutterstock
Read the full article: How Do Delivery Robots Work? How They Safely Deliver Your Packages
Facebook wants to make AI better by asking people to break it
The explosive successes of AI in the last decade or so are typically chalked up to lots of data and lots of computing power. But benchmarks also play a crucial role in driving progress—tests that researchers can pit their AI against to see how advanced it is. For example, ImageNet, a public data set of 14 million images, sets a target for image recognition. MNIST did the same for handwriting recognition and GLUE (General Language Understanding Evaluation) for natural-language processing, leading to breakthrough language models like GPT-3.
A fixed target soon gets overtaken. ImageNet is being updated and GLUE has been replaced by SuperGLUE, a set of harder linguistic tasks. Still, sooner or later researchers will report that their AI has reached superhuman levels, outperforming people in this or that challenge. And that’s a problem if we want benchmarks to keep driving progress.
So Facebook is releasing a new kind of test that pits AIs against humans who do their best to trip them up. Called Dynabench, the test will be as hard as people choose to make it.
Benchmarks can be very misleading, says Douwe Kiela at Facebook AI Research, who led the team behind the tool. Focusing too much on benchmarks can mean losing sight of wider goals. The test can become the task.
“You end up with a system that is better at the test than humans are but not better at the overall task,” he says. “It’s very deceiving, because it makes it look like we’re much further than we actually are.”
Kiela thinks that’s a particular problem with NLP right now. A language model like GPT-3 appears intelligent because it is so good at mimicking language. But it is hard to say how much these systems actually understand.
Think about trying to measure human intelligence, he says. You can give people IQ tests, but that doesn’t tell you if they really grasp a subject. To do that you need to talk to them, ask questions.
Dynabench does something similar, using people to interrogate AIs. Released online today, it invites people to go to the website and quiz the models behind it. For example, you could give a language model a Wikipedia page and then ask it questions, scoring its answers.
In some ways, the idea is similar to the way people are playing with GPT-3 already, testing its limits, or the way chatbots are evaluated for the Loebner Prize, a contest where bots try to pass as human. But with Dynabench, failures that surface during testing will automatically be fed back into future models, making them better all the time.
For now Dynabench will focus on language models because they are one of the easiest kinds of AI for humans to interact with. “Everybody speaks a language,” says Kiela. “You don’t need any real knowledge of how to break these models.”
But the approach should work for other types of neural network too, such as speech or image recognition systems. You’d just need a way for people to upload their own images—or have them draw things—to test it, says Kiela: “The long-term vision for this is to open it up so that anyone can spin up their own model and start collecting their own data.”
“We want to convince the AI community that there’s a better way to measure progress,” he adds. “Hopefully, it will result in faster progress and a better understanding of why machine-learning models still fail.”
In Praise Of The DT830, The Phenomenal Instrument You Probably Don’t Recognise For What It Is
If we had to make a guess at the single piece of electronic bench equipment owned by the highest proportion of Hackaday readers, it would not be a budget oscilloscope from Rigol, nor would it be a popular portable soldering iron like the TS100. Instead we’re guessing that it’s a multimeter, and not even the most accomplished one.
The DT830 is a genericised Chinese-manufactured 3.5 digit digital multimeter that can be had for an astonishingly low price. Less than a decent hamburger gets you an instantly recognisable plastic case with a chunky rotary range selector switch, and maybe a socket for some kind of transistor or component tester. Make sure that there is a 9 volt battery installed, plug in the pair of test leads, and you’re in business for almost any day-to-day electrical or electronic measurement. They’ve been available in one form or another for decades and have been the subject of innumerable give-aways and loss-leader offers, so it’s a reasonsble guess that you’ll have one somewhere. I have three as far as I know, they make great on-the-go instruments and have proved themselves surprisingly reliable for what they are.
Persuading You Is Going To Be A Tough Sell
If you talk about the DT830 in polite company, you might be greeted with snorts of derision. It’s not difficult to find reviews that tear one down and compare it to a more expensive meter, and not surprisingly find the pricey meter to be of higher quality.
And it’s certainly true that for a couple of dollars, you get a switch that won’t last forever and high voltage isolation that maybe isn’t quite up to spec. But I’m going to advance a different take on the DT830 that may surprise some of you: to me it’s a modern classic, an instrument that provides performance for its price that is nothing short of phenomenal. Because that pocket-money meter not only measures voltage, current, and resistance, it does so accurately and repeatably, and to compare that with what might have gone before is to show just much better a device it is.
Thirty years ago, a digital multimeter was an expensive item, and most multimeters were still analogue. A cheap multimeter was therefore invariably a small pocket analogue device, and the very cheap ones could be astoundingly awful. Accuracy and repeatability in reading wasn’t their strong point, and while I am a great fan of analogue multimeters when it comes to spotting dips and trends in tweaking analogue circuitry, even I can’t find reason to praise the inexpensive ones. By comparison the DT830 delivers reliable and accurate readings with a high-impedance input, something I would have given a lot for in 1985.
That Performance Is No Fluke
So given that it costs considerably less than a pint of beer in a British pub, how does such a cheap instrument do it? The answer is, by standing on the shoulders of giants. My colleague Anool Mahidharia supplied the answer here back in 2017 when he took a look at the Intersil 71XX series of integrated circuits; the archetypal DT830 contains an ICM 7106 3.5 digit digital panel meter chip, whose roots lie in a much more exclusive stratum of the industry.
(Despite there being a load of newer and more accomplished multimeter chips on the market I was surprised to find that none of them had found their way into the meters I’d opened.)
The ICM 7106 was based on work Intersil did in 1977 to produce the part in Fluke’s first portable DMM, the model 8020A.
So you’re not getting anywhere near the physical design or component quality of that expensive meter, but you are benefiting from the tech that made its ancestor a very good instrument for the 1970s. The dual-slope integrating ADC and precision reference are the same as the ones in many far more expensive meters, which is what makes the reading from your few-dollar DT830 one you can trust. Not bad for something you might dismiss as a piece of junk!
If there is something to be gleaned from this story, it is a very real demonstration of the power of semiconductor manufacturing. Assuming it has passed acceptable factory QA, every 7106 is as good as any other 7016, from the first one made by Intersil in the 1970s through to the unknown-origin chip hiding under an epoxy blob in my cheap meter. The manufacturer can skimp on every other component in the meter, but assuming that there’s no money in counterfeiting a 43-year-old chip that long ago left its premium product phase behind and has been manufactured by many sources over the years, they can’t skimp on the chip that powers it. To be an ICM7106, it must have the same features as the original from the 1970s, thus my bargain-basement meter still shares something that matters with one of far higher quality.
The DT830 multimeter, then. It may be a heap of junk, but it’s an astonishingly good heap of junk. I for one, salute it.
Advice needed. I’m a cheapskate and I have a compulsive tendency to NOT SPEND any money at all.
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