Network problems? What could be the reason? It is just like being a detective and solve a mystery case – but who wants to be a James Bond when you’ve got a software like Why Can’t I Connect. WCIC is an easy and handy tool that lets you diagnose network issues and even helps you resolve TCP/IP connection errors. This tool will let you connect to various kinds of servers and would perform an incoming and outgoing test to diagnose the network for any problems and related issues.
WCIC is an open-sourced utility licensed under GNU public license. It is easy to use and operate the utility. It has basic and essential features that are very useful while diagnosing network problems in different types of servers.
Using this software you can connect to the following types of servers:
Microsoft SQL Server: WCIC would create a TCP/IP connection to any Microsoft SQL Server you want. What you simply need to enter is IP Address and Port of the server. But remember WCIC would not verify the server username or password, it would only make a connection to the server.
MySQL Server: It would make a similar connection as it made with Microsoft MySQL Server.
FTP and SFTP: WCIC can even diagnose network problems with FTP and Secure FTP servers. Simply you need to enter the IP address and the port only!
POP3 and IMAP: Email protocols like POP3 and IMAP can even be diagnosed using this wonderful software, for these you need to enter the server IP address and choose between STARTTLS and SSL/TLS and enter the corresponding port numbers. But remember it would not attempt to verify the username and password.
It can even diagnose other servers like IRC, LDAP and Usenet. Why Can’t I connect to create a complete log of performed operations and you can export the log by copying everything, and you can save it as a record for the future.
Overall Why Can’t I Connect is a must-have utility, as it has the essential features that are required while diagnosing the network problems experienced in different servers – and it is even useful for various testing features like if you want to test whether a server is live or not. It is easy to use and doesn’t require any geeky configuration and commands.
Click here to obtain Why Can’t I Connect.
The built-in Network & Internet Diagnostic & Repair Tool is another tool that you may want to have a look at.
If you have just upgraded to Windows 11, you may have noticed that the Settings app looks and feels very different from what you were used to in Windows 10. The reason for this is that each Setting app in a new operating system has its own style.
In this blog post, we will explore what’s new in the Bluetooth and devices panels in Windows Settings. Here you’ll also learn how to use the Devices Panel in Settings, including Bluetooth, Printers & scanners, Your Phone, Cameras, Mouse, Touchpad, Pen & Windows Ink, Autoplay, and USB. To clarify any confusion, we’ve included a few images.
This new operating system comes with a completely updated Settings app that accounts for most of the visual changes in this update. With the new application, you can easily modify system settings and customize them.
To get it started, open the Windows 11 Settings menu first using the Windows+I keyboard shortcut. Then select the Bluetooth & devices tab from the left pane. From here, we will cover all the sections under Bluetooth & devices and explain them in detail. Windows 11 Settings includes the following sections under the Bluetooth & devices tab.
The Windows 11 Settings app has undergone some changes. One of the major changes in the new Bluetooth and devices tab in the Settings menu. Here, we’ll take a look at what you can find in this new interface and how to get around quickly. Below is a detailed explanation of each section listed above:
In this section, you can turn on or off Bluetooth settings to connect the nearby Bluetooth devices. Here the paired devices will automatically connect as soon as Bluetooth is activated.
In the Devices tab, you will find details about the Bluetooth devices connected to your PC like the mouse, audio, pen, keyboard, displays and docks, and others.
If you want to add a new device, click on Add device and choose the device you want to add. As you open this section, you will see the connected devices listed here. You can remove paired devices by clicking on the three dots next to each device and selecting the Remove device option.
Furthermore, there are some advanced options related to Sound and Display. The Sound section helps you choose a device for recording and speaking, troubleshoot common sound problems, and more. If you go to Display settings, you can adjust the color and brightness of the screen as well as the device’s size and layout. In the brightness setting, you can determine the brightness of the built-in display. There is also an option called Night Light which allows you to use warmer colors to block blue light.
Changing the size of text, apps, and other items is possible under the Scale & layout section. Additionally, you can adjust the resolution to fit the connected displays.
The Printers and Scanners tab shows all the printers and scanners connected to your computer. If you click on the Add device button, you can also add a printer or a scanner. Here you will find that the device is configured for Microsoft Print to PDF, Microsoft XPS Document Writer, and Send to OneNote 2016.
Under the Printer preferences section, you can see a button where you can turn on / off the metered connection for downloading drivers and device software.
With this button turned off, you will save your data when you are on a metered connection. Additionally, Windows can be set here to manage your default printer.
Using this tab, you get instant access to the photos and texts on your Android device. In this way, you can avoid constantly checking for messages, receiving alerts, or even making or receiving calls on your Smartphone.
In addition to this, it is also possible to enable or disable suggestions for Android phones when used with Windows.
There is a new camera tab included in Windows 11 Settings that provides settings for managing cameras and setting up network cameras.
You can manually adjust brightness, contrast, and even the Camera privacy settings, which let you choose which apps are allowed access to your camera.
On this tab, you can select either the left or right mouse buttons as your primary buttons. In addition, here you can change the speed at which the mouse points. It is possible here to adjust the scrolling rate of the mouse wheel so that you can scroll multiple lines at once or one screen at a time.
The number of lines you want to scroll each time can range from 1 to 100. In addition, you can toggle the ability to scroll inactive windows when you hover over them.
The gestures and interactions of your laptop’s touchpad can be configured here. There are four different options for adjusting the touchpad’s sensitivity: most, high, medium, and low. We recommend you set the sensitivity to Medium since it prevents the cursor from moving too fast.
On the Pen & Windows Ink page, you can choose the font to use for handwriting. Additionally, you can check or uncheck the box if you’d like to write with your fingertip in the handwriting panel.
With this tab, you are able to use the AutoPlay feature across all media and devices. When you connect a USB drive, memory card, or other media to your computer, AutoPlay allows you to decide what to do by default.
For those of you who do not find this feature useful, or would prefer a different default action when you connect a USB flash drive or other media or device, the Default app settings page makes it easy to manage this feature.
This tab shows you an alert in case there are issues with connecting a USB device. For this feature to work, it is necessary to turn on the toggle next to the Connection notifications option.
Here are the steps you can follow to locate the Bluetooth option on your new Windows 11 PC:
The following steps will help you update the Bluetooth drivers on Windows 11.
Problems with Bluetooth drivers are likely to be the cause of missing Bluetooth devices. You can update the Bluetooth drivers to resolve the issue. The following steps will guide you through updating Bluetooth drivers in Windows 11:
Important! Duo two-factor authentication is now required for off-campus access to VDI.
Complete instructions for downloading, installation, troubleshooting, and web version of VDI Client: VMWare View Client Setup and Usage (pdf)
Select one of the following links:
Download VMware Client or use HTML web-based access
Download/Open VMware App
Download/Open VMware App
For mobile devices: Open the VMware app, enter server name vdesktop.wm.edu, enter your W&M Username and password. Make sure the domain is set to Campus.
Mac users: if you are having issues with the VMware Horizon Client, please obtain the latest version from vdesktop.wm.edu by clicking on Install VMware Horizon Client.
Virtual Desktop Infrastructure (VDI) allows you to log into a virtual desktop with a standard William & Mary computer image. It's similar to logging in to a lab computer or classroom podium computer. Network drives, Microsoft Office software, standard computer applications, and various browsers are all available through VDI.
VDI offers a secure environment to access certain IT services. An app for tablet computers and smart phones for both Android and iOS devices (iPad, iPhone, etc.) makes using VDI easy while on-the-go.
VDI can be accessed by downloading the VMWare View client or by using a web-based version. The web version can be a more convenient option for some users, but it has limitations, including slower performance than the client version and the inability to use thumb drives. If you choose to obtain the client, you may obtain it to the device(s) of your choice: a laptop, home computer, iPad/iPhone, Android phone, etc.
To access VDI, go to https://vdesktop.wm.edu/. You will be presented with the two options: to Install the View Client or HTML Access. Choose HTML Access for the web-based version of VDI.
Complete instructions for downloading, installation, troubleshooting, and web version: VMWare View Client Setup and Usage (pdf)
For mobile devices, you can obtain VMware Horizon View Client from the app store. Use the same instructions (above) for installation.
*Off-Campus Access: If you are accessing VDI from off-campus, you will login using Duo Two-Factor authentication. First, you must enroll in Duo at: https://2f.wm.edu/. Then you can follow the access instructions found here:
VDI is available at all times, however there are a couple things to keep in mind that might limit your use.
Virtual desktops are arranged in different "pools" to which different groups are given access. For instance, there are 50 virtual desktops in the Faculty/Staff desktop pool. Any employee may access desktops in this pool, as well as affiliates on a case-by-case basis. Since there are 50 desktops in this pool, there can be a maximum of 50 people logged in to the VDI FacStaff desktops at one time. Once 50 is reached, you will not be able to log in and must wait until someone logs off to use the system.
Similarly, the business desktop pool contains 50 desktops for select business students and faculty. Requests for access to this pool must come from business school instructors.
Time is also a limiting factor. An active session will be available for 10 hours (no matter what pool or desktop you are signed into). After 10 hours (regardless of activity level) the session will be logged-off and disconnected automatically. The screen will automatically lock if the session has been inactive (no keyboard or mouse movements) for 20 minutes. Enter your W&M Username and Password to resume your session.
You will be able to see the network drives you have permissions to, like your personal H: drive. If you have access to your department's group drive (G: drive), you will be able to access it as well.
You will not be able to access your office computer's C: drive. The C: drive you see on VDI is a virtual C: drive, not the one on your office computer. Be careful not to save any information to this C: drive, as it will be lost.
All software change requests must go through a license review process, even if it is "freeware". After that, we need to test the software in the VDI environment to make sure it is compatible. Finally, we have to recompile the VDI desktops to include the requested software.
For these reasons, we cannot honor software and software version change requests on short notice. We ask as much advance notice as possible, but at least 2 months is required.
We will make every reasonable effort to integrate your software, but please be aware that not all software will work in VDI.
The VDI desktops use "folder redirection", meaning anything you save to the following locations will actually save to corresponding folders on your H: drive:
If you want to verify that a file you saved in one of these locations is safe on your H: drive, access the H: drive (for example, via the "Computer" link on the Start Menu) and verify your file is there. However, if you save a file just under your user folder (such as C:/users/my_user), you are saving it on the C: drive, and it will be lost.
Important! You MUST save all your work either to a network drive or a USB (thumb) drive. Any data saved to the virtual C: drive WILL BE LOST.
To save to a usb (thumb) drive, plug it into into your computer and use the "Connect USB Device" option (at the top-left of the VDI window) to connect it to your virtual desktop.
You can also print from VDI. Simply connect the device you are using to a printer and then you can print from the VDI to that printer.
If you have problems logging in to VDI verify your W&M Username and password are correct, as well as the correct domain (CAMPUS). Also verify that you are authorized to use VDI (W&M Faculty/Staff). If you are accessing VDI from off-campus, you must first enroll in Duo two-factor authentication at: https://2f.wm.edu/.
Another issue may be that a virtual desktop may not be available. There is a maximum number of concurrent sessions at any given time. If all sessions are taken, you must wait for someone to log-off before you can log-in.
Support is available during normal business hours through the Technology Support Center.
Need help? Contact the Technology Support Center (TSC)
757-221-4357 (HELP) | email@example.com | Jones 201, Monday - Friday 8:00 am - 5:00 pm
Easy-to-use software, affordable ink, a long warranty, and plenty of thoughtful touches make this inkjet all-in-one less annoying than the competition. Results look sharp, too.
*At the time of publishing, the price was $230.
|Type:||Inkjet||Size:||17.3 x 13.48 x 10.94 in|
|Features:||Print, copy, fax, scan||Color Print:||Yes|
|Wireless:||Yes||Cost per page:||2.2¢ per black and 8.9¢ for color|
The HP OfficeJet Pro 9015e is likely to be the easiest printer you’ve ever had to set up, and that alone is enough to recommend it. But it also prints beautifully (and quickly), scans well, has great apps for PCs and mobile devices, and prints for an affordable 2.2¢ per page in black or 8.9¢ per page in color. If you print a lot of photos, you can opt for HP’s Instant Ink program (a six-month trial is included with your initial purchase), which brings the cost of each color page to as little as 2.9¢, including glossies. It looks great in any office, thanks to a clean, compact design, and it comes with a two-year warranty that’s twice as long as what you’d get with most competing printers. The 9015e replaces our former pick, the OfficeJet Pro 9015, but it’s identical from a hardware perspective; the only differences are the longer warranty, the longer Instant Ink trial, and some added software features that are bundled into the new HP+ printing ecosystem. If you’re not interested in the extras HP+ has to offer, the older 9015 is a great machine that you might be able to find at a discount.
Brother’s entry-level AIO isn’t the fastest, best designed, or easiest to use, but it is cheap to operate, and it still produces great-looking prints and scans.
*At the time of publishing, the price was $130.
|Type:||Inkjet||Size:||17.3 x 13.48 x 10.94 in|
|Features:||Print, copy, fax, scan||Color Print:||Yes|
|Wireless:||Yes||Cost per page:||2.2¢ per black-and-white and 8.9¢ for color|
If you just want the cheapest prints possible and don’t care about speed, fancy apps, or looks, the Brother MFC-J805DW is an excellent choice. At a mere 0.9¢ per black-and-white page and 4.7¢ for color, it’s one of the most cost-efficient printers you can buy, and the results look great, too. You’d wait longer to get them than you would with the HP 9015e, but for casual use that isn’t a big deal.
This business-class machine checks all the boxes for a home office or small business: It’s faster, sharper, more durable, and more secure than our other picks.
*At the time of publishing, the price was $600.
|Type:||Laserjet||Size:||16.4 x 18.6 x 15.7 in|
|Features:||Print, copy, fax, scan||Color Print:||Yes|
|Wireless:||Yes||Cost per page:||2.3¢ per black and 14¢ for color|
If your work finds you printing and scanning all day, every day, you should be willing to upgrade to a business-oriented color laser AIO like the HP Color LaserJet Pro MFP M479fdw. It prints and scans faster, sharper, and more reliably than inkjet alternatives, and it includes robust admin and security settings designed for situations that may involve sensitive data. We don’t think it’s necessary for most homes or even the average home office. But if you run a business with modest printing and paper-handling needs, or if you’ve grown exasperated with your inkjet AIO’s failings, the M479fdw should hit the sweet spot.
Surgery is scary. It usually involves having your body cut open, and sometimes things go wrong. You react badly to anesthesia, or suffer breathing or heart problems. Or maybe the surgeon nicks a blood vessel, leaves an instrument inside, or even operates on the wrong body part.
Less dramatic but often as serious and far more common is when things go wrong after you leave the operating room. Up to 30 percent of patients suffer infections, heart attacks, strokes, or other complications after surgery and sometimes even die as a result. That’s what happened to Marvin Birnbaum, a retired New York City court reporter, after he developed an infection following hip replacement surgery, his daughter Jacqueline says.
Perhaps scariest of all, though many hospitals now gather data on those problems, patients for the most part remain in the dark about surgical safety. Industry insiders have access to some of that information because hospitals track how well patients do and report results to state and national officials.
Plus, some hospitals submit data to national registries so that they can see how they stack up against one another. But that safety information remains largely hidden from patients.
“Consumers have very little to go on when trying to select a hospital for surgery, not knowing which ones do a good job at keeping surgery patients safe and which ones don’t,” says Lisa McGiffert, director of Consumers Union’s Safe Patient Project. “They might as well just throw a scalpel at a dartboard.”
Our new surgery Ratings are part of an ongoing effort to shed light on hospital quality and to push the health care industry toward more transparency. “Because patients and their families shouldn’t have to make such important decisions with so little information,” McGiffert says.
Our Ratings for the first time make public a measure that some hospitals now use to track quality—the percentage of Medicare patients undergoing surgery who die in the hospital or stay longer than expected.
We looked at results for 27 kinds of scheduled surgeries, which we combined into an overall surgery Rating, and also developed Ratings for five of those procedures: back surgery, replacements of the hip or knee, and procedures to remove blockages in arteries in the heart (angioplasty) or neck (carotid artery surgery).
To develop the Ratings, we worked with MPA, a health care consulting firm with expertise in analyzing medical claims and clinical records. This project uses billing claims that hospitals submitted to Medicare for patients 65 and older, from 2009 through 2011, and covers 2,463 hospitals in all 50 states plus Washington, D.C., and Puerto Rico.
Consumers have had little to go on when choosing a hospital for surgery.
“The beauty of this approach is that preventable complications correlate with post-operative length of stay,” says Arnold Millstein, M.D., M.P.H., director of the Clinical Excellence Research Center at Stanford University. He was not involved in our analysis but has studied how hospitals measure and Strengthen quality. “This is about as good as complications measurement can be when using existing claims data,” he says.
Some experts say that may not be good enough. For one thing, factors other than complications can contribute to extended hospital stays. In addition, “we are concerned that the methods used to generate these performance ratings have not been validated against gold-standard measures,” says David M. Shahian, M.D., vice president of the Lawrence Center for Quality and Safety at Massachusetts General Hospital. “They are based on claims data rather than clinical data from patient records.”
Finally, our surgery Ratings are just one indication of a hospital’s performance. “There are a lot of dimensions to hospital quality, and no single measure captures everything,” says Peter Cram, M.D., director of general medicine at the University of Iowa Carver College of Medicine.
But we think our Ratings offer vital information to patients and hospitals. “We wish we had access to more comprehensive, standardized information, but this is the best that is available,” says John Santa, M.D., M.P.H., medical director of Consumer Reports Health. “Our surgery Ratings provide patients more information so that they can make informed choices before surgery,” he adds. “And we hope that by highlighting performance differences, we can motivate hospitals to improve.”
Click on the map at right to find Ratings of hospitals nationwide. The Ratings include include information on our surgery Ratings, our hospital Safety Score, as well as some information on performance for more than 4,000 hospitals.
You can also obtain a PDF showing the overall surgery Ratings for 2,463 across the country.
Printers are annoying. All of them. But if you want to keep your annoyance to a minimum, we recommend a laser printer: Not only do laser models print sharp text and crisp graphics, but they also run more reliably than inkjets and won’t clog if they sit unused for weeks between jobs. The best laser printer is the powerful, versatile HP Color LaserJet Pro M255dw. It’s easy to set up and simple to use, and it produces great-looking results, both in color and in black and white.
Global supply chain issues have made it more difficult to find some of our printer picks, and have caused the price of others to jump. As of this writing, our budget pick is out of stock, but all Brother L2300-series models will get you similar print performance with slight speed or feature differences. The HL-L2370DW is a particularly close relative that seems to be more readily available at the moment. If you’re considering other printers in this series, just be aware that the letters after the number indicate key features: D for duplex printing and W for wireless. Some models drop one or the other, so be sure to check before buying.
The HP M255dw has an intuitive touchscreen interface, great apps, and a low cost of operation. It produces great results, too: crisp black text and vibrant color graphics. A fall 2020 software update locked out non-HP toner, so be prepared to have to pay full price when you need to replace the cartridges.
*At the time of publishing, the price was $300.
If you’re looking for a laser printer that can handle everything from book reports to corporate reports without driving you crazy in the process, the HP Color LaserJet Pro M255dw is the one to get. It stands out from the competition with an easy-to-use, smartphone-style touch interface and 21st-century mobile and PC software that makes daily use far less frustrating than on other printers we’ve tried. In our tests, it produced sharp black text, vibrant full-color graphics, and even photos good enough for a school report. It’s fast, topping out at around 17 pages per minute, and it can print on envelopes, labels, and other odd-size media thanks to a handy bypass slot.
With low operating costs, quick operation, and useful features, the HL-L2350DW is the best laser printer you can get for around $100.
*At the time of publishing, the price was $110.
Some people just need a cheap laser printer for occasional black-and-white print jobs. For them, we recommend the Brother HL-L2350DW. Setup is painless, and the machine is compatible with all major platforms, including Windows, macOS, Chrome OS, Linux, iOS, and Android. Its cost per page is a reasonable 3.3¢, it sticks to Wi-Fi like glue, and its price generally hovers around $100. Its print quality is merely adequate right out of the box, but you can Strengthen that with a simple tweak to the toner density setting. Just be aware that the HL-L2350DW can’t scan or copy; if you need that functionality, look to our monochrome all-in-one pick.
This multifunction printer adds a flatbed scanner and an automatic document feeder to the HL-L2350DW, significantly upping its home-office utility.
*At the time of publishing, the price was $200.
If you like the sound of our budget pick but want the ability to scan and copy documents and photos too, the Brother MFC-L2750DW should fit the bill. At its core it’s a very similar printer—and it’s just as easy to set up—but it also has a flatbed scanner and a fast, single-pass duplexing automatic document feeder on top. Its print quality is slightly better out of the box, and you get the same operating costs, the same print speed, and the same connectivity options as you do with the HL-L2350DW. For home offices this model is a great do-it-all option—as long as you don’t need color.
This business-class machine checks all the boxes for a home office or small business: It’s faster, sharper, more durable, and more secure than our other picks. Like our top pick, it requires you to use official HP toner.
*At the time of publishing, the price was $450.
For a small business with more serious productivity needs, the HP Color LaserJet Pro MFP M479fdw is a worthwhile upgrade over our other picks. It prints and scans more quickly and more reliably than inkjet alternatives, produces sharper results, and includes robust admin and security settings designed for situations that may involve sensitive data. All-in-one color lasers like the M479fdw cost more and are more expensive to operate than inkjet printers with comparable features, but they deliver high-quality color prints, copies, and scans at a quicker pace than cheaper models. They’re also sturdier and more reliable than inkjets.
AI safety is vital.
You would be hard-pressed to seemingly argue otherwise.
As readers of my columns know well, I have time and again emphasized the importance of AI safety, see the link here. I typically bring up AI safety in the context of autonomous systems, such as autonomous vehicles including self-driving cars, plus amidst other robotic systems. Doing so highlights the potential life-or-death ramifications that AI safety imbues.
Given the widespread and nearly frenetic pace of AI adoption worldwide, we are facing a potential nightmare if suitable AI safety precautions are not firmly established and regularly put into active practice. In a sense, society is a veritable sitting duck as a result of today’s torrents of AI that poorly enact AI safety including at times outright omitting sufficient AI safety measures and facilities.
Sadly, scarily, attention to AI safety is not anywhere as paramount and widespread as it needs to be.
In my coverage, I have emphasized that there is a multitude of dimensions underlying AI safety. There are technological facets. There are the business and commercial aspects. There are legal and ethical elements. And so on. All of these qualities are interrelated. Companies need to realize the value of investing in AI safety. Our laws and ethical mores need to inform and promulgate AI safety considerations. And the technology to aid and bolster the adoption of AI safety precepts and practices must be both adopted and further advanced to attain greater and greater AI safety capabilities.
When it comes to AI safety, there is never a moment to rest. We need to keep pushing ahead. Indeed, please be fully aware that this is not a one-and-done circumstance but instead a continual and ever-present pursuit that is nearly endless in always aiming to improve.
I’d like to lay out for you a bit of the AI safety landscape and then share with you some key findings and crucial insights gleaned from a recent event covering the latest in AI safety. This was an event last week by the Stanford Center for AI Safety and took place as an all-day AI Safety Workshop on July 12, 2022, at the Stanford University campus. Kudos to Dr. Anthony Corso, Executive Director of the Stanford Center for AI Safety, and the team there for putting together an excellent event. For information about the Stanford Center for AI Safety, also known as “SAFE”, see the link here.
First, before diving into the Workshop results, let’s do a cursory landscape overview.
To illustrate how AI safety is increasingly surfacing as a vital concern, let me quote from a new policy paper released just earlier this week by the UK Governmental Office for Artificial Intelligence entitled Establishing a Pro-innovation Approach to Regulating AI that included these remarks about AI safety: “The breadth of uses for AI can include functions that have a significant impact on safety - and while this risk is more apparent in certain sectors such as healthcare or critical infrastructure, there is the potential for previously unforeseen safety implications to materialize in other areas. As such, whilst safety will be a core consideration for some regulators, it will be important for all regulators to take a context-based approach in assessing the likelihood that AI could pose a risk to safety in their sector or domain, and take a proportionate approach to manage this risk.”
The cited policy paper goes on to call for new ways of thinking about AI safety and strongly advocates new approaches for AI safety. This includes boosting our technological prowess encompassing AI safety considerations and embodiment throughout the entirety of the AI devising lifecycle, among all stages of AI design, development, and deployment efforts. I will next week in my columns be covering more details about this latest proposed AI regulatory draft. For my prior and ongoing coverage of the somewhat akin drafts regarding legal oversight and governance of AI, such as the USA Algorithmic Accountability Act (AAA) and the EU AI Act (AIA), see the link here and the link here, for example.
When thinking mindfully about AI safety, a fundamental coinage is the role of measurement.
You see, there is a famous generic saying that you might have heard in a variety of contexts, namely that you cannot manage that for which you don’t measure. AI safety is something that needs to be measured. It needs to be measurable. Without any semblance of suitable measurement, the question of whether AI safety is being abided by or not becomes little more than a vacuous argument of shall we say unprovable contentions.
Sit down for this next point.
Turns out that few today are actively measuring their AI safety and often do little more than a wink-wink that of course, their AI systems are embodying AI safety components. Flimsy approaches are being used. Weakness and vulnerabilities abound. There is a decided lack of training on AI safety. Tools for AI safety are generally sparse or arcane. Leadership in business and government is often unaware of and underappreciates the significance of AI safety.
Admittedly, that blindness and indifferent attention occur until an AI system goes terribly astray, similar to when an earthquake hits and all of a sudden people have their eyes opened that they should have been preparing for and readied to withstand the shocking occurrence. At that juncture, in the case of AI that has gone grossly amiss, there is frequently a madcap rush to jump onto the AI safety bandwagon, but the impetus and consideration gradually diminish over time, and just like those earthquakes is only rejuvenated upon another big shocker.
When I was a professor at the University of Southern California (USC) and executive director of a pioneering AI laboratory at USC, we often leveraged the earthquake analogy since the prevalence of earthquakes in California was abundantly understood. The analogy aptly made the on-again-off-again adoption of AI safety a more readily realized unsuitable and disjointed way of getting things done. Today, I serve as a Stanford Fellow and in addition serve on AI standards and AI governance committees for international and national entities such as the WEF, UN, IEEE, NIST, and others. Outside of those activities, I recently served as a top executive at a major Venture Capital (VC) firm and today serve as a mentor to AI startups and as a pitch judge at AI startup competitions. I mention these aspects as background for why I am distinctly passionate about the vital nature of AI safety and the role of AI safety in the future of AI and society, along with the need to see much more investment into AI safety-related startups and related research endeavors.
All told, to get the most out of AI safety, companies and other entities such as governments need to embrace AI safety and then enduringly stay the course. Steady the ship. And keep the ship in top shipshape.
Let’s lighten the mood and consider my favorite talking points that I use when trying to convey the status of AI safety in contemporary times.
I have my own set of AI safety levels of adoption that I like to use from time to time. The idea is to readily characterize the degree or magnitude of AI safety that is being adhered to or perhaps skirted by a given AI system, especially an autonomous system. This is just a quick means to saliently identify and label the seriousness and commitment being made to AI safety in a particular instance of interest.
I’ll briefly cover my AI safety levels of adoption and then we’ll be ready to switch to exploring the recent Workshop and its related insights.
My scale goes from the highest or topmost of AI safety and then winds its way down to the lowest or worst most of AI safety. I find it handy to number the levels and ergo the topmost is considered as rated 1st, while the least is ranked as last or 7th. You are not to assume that there is a linear steady distance between each of the levels thus keep in mind that the effort and degree of AI safety are often magnitudes greater or lesser depending upon where in the scale you are looking.
Here's my scale of the levels of adoption regarding AI safety:
1) Verifiably Robust AI Safety (rigorously provable, formal, hardness, today this is rare)
2) Softly Robust AI Safety (partially provable, semi-formal, progressing toward fully)
3) Ad Hoc AI Safety (no consideration for provability, informal approach, highly prevalent today)
4) Lip-Service AI Safety (smattering, generally hollow, marginal, uncaring overall)
5) Falsehood AI Safety (appearance is meant to deceive, dangerous pretense)
6) Totally Omitted AI Safety (neglected entirely, zero attention, highly risk prone)
7) Unsafe AI Safety (role reversal, AI safety that is actually endangering, insidious)
Researchers are usually focused on the topmost part of the scale. They are seeking to mathematically and computationally come up with ways to devise and ensure provable AI safety. In the trenches of everyday practices of AI, regrettably Ad Hoc AI Safety tends to be the norm. Hopefully, over time and by motivation from all of the aforementioned dimensions (e.g., technological, business, legal, ethical, and so on), we can move the needle closer toward the rigor and formality that ought to be rooted foundationally in AI systems.
You might be somewhat taken aback by the categories or levels that are beneath the Ad Hoc AI Safety level.
Yes, things can get pretty ugly in AI safety.
Some AI systems are crafted with a kind of lip-service approach to AI safety. There are AI safety elements sprinkled here or there in the AI that purport to be providing AI safety provisions, though it is all a smattering, generally hollow, marginal, and reflects a somewhat uncaring attitude. I do not want to though leave the impression that the AI developers or AI engineers are the sole culprits in being responsible for the lip-service landing. Business or governmental leaders that manage and oversee AI efforts can readily usurp any energy or proneness toward the potential costs and resource consumption needed for embodying AI safety.
In short, if those at the helm are not willing or are unaware of the importance of AI safety, this is the veritable kiss of death for anyone else wishing to get AI safety into the game.
I don’t want to seem like a downer but we have even worse levels beneath the lip-service classification. In some AI systems, AI safety is put into place as a form of falsehood, intentionally meant to deceive others into believing that AI safety embodiments are implanted and actively working. As you might expect, this is rife for dangerous results since others are bound to assume that AI safety exists when it in fact does not. Huge legal and ethical ramifications are like a ticking time bomb in these instances.
Perhaps nearly equally unsettling is the entire lack of AI safety all told, the Totally Omitted AI Safety category. It is hard to say which is worse, falsehood AI safety that maybe provides a smidgeon of AI safety despite that it overall falsely represents AI safety or the absolute emptiness of AI safety altogether. You might consider this to be the battle between the lesser of two evils.
The last of the categories is really chilling, assuming that you are not already at the rock bottom of the abyss of AI safety chilliness. In this category sits the unsafe AI safety. That seems like an oxymoron, but it has a straightforward meaning. It is quite conceivable that a role reversal can occur such that an embodiment in an AI system that was intended for AI safety purposes turns out to ironically and hazardously embed an entirely unsafe element into the AI. This can especially happen in AI systems that are known as being dual-use AI, see my coverage at the link here.
Remember to always abide by the Latin vow of primum non nocere, which specifically instills the classic Hippocratic oath to make sure that first, do no harm.
There are those that put in AI safety with perhaps the most upbeat of intentions, and yet shoot their foot and undermine the AI by having included something that is unsafe and endangering (which, metaphorically, shoots the feet of all other stakeholders and end-users too). Of course, evildoers might also take this path, and therefore either way we need to have suitable means to detect and verify the safeness or unsafe proneness of any AI — including those portions claimed to be devoted to AI safety.
It is the Trojan Horse of AI safety that sometimes in the guise of AI safety the inclusion of AI safety renders the AI into a horrendous basket full of unsafe AI.
Okay, I trust that the aforementioned overview of some trends and insights about the AI safety landscape has whetted your appetite. We are now ready to proceed to the main meal.
Recap And Thoughts About The Stanford Workshop On AI Safety
I provide next a brief recap along with my own analysis of the various research efforts presented at the recent workshop on AI Safety that was conducted by the Stanford Center for AI Safety.
You are stridently urged to read the related papers or view the videos when they become available (see the link that I earlier listed for the Center’s website, plus I’ve provided some additional links in my recap below).
I respectively ask too that the researchers and presenters of the Workshop please realize that I am seeking to merely whet the appetite of readers or viewers in this recap and am not covering the entirety of what was conveyed. In addition, I am expressing my particular perspectives about the work presented and opting to augment or provide added flavoring to the material as commensurate with my existing style or panache of my column, versus strictly transcribing or detailing precisely what was pointedly identified in each talk. Thanks for your understanding in this regard.
I will now proceed in the same sequence of the presentations as they were undertaken during the Workshop. I list the session title, and the presenter(s), and then share my own thoughts that both attempt to recap or encapsulate the essence of the matter discussed and provide a tidbit of my own insights thereupon.
Presentation by Dr. Marco Pavone
Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University, and Director of Autonomous Vehicle Research at NVIDIA, plus Director of the Stanford Autonomous Systems Laboratory and Co-Director of the Center for Automotive Research at Stanford
Here’s my brief recap and erstwhile thoughts about this talk.
A formidable problem with contemporary Machine Learning (ML) and Deep Learning (DL) systems entails dealing with out-of-distribution (OOD) occurrences, especially in the case of autonomous systems such as self-driving cars and other self-driving vehicles. When an autonomous vehicle is moving along and encounters an OOD instance, the responsive actions to be undertaken could spell the difference between life-or-death outcomes.
I’ve covered extensively in my column the circumstances of having to deal with a plethora of fast-appearing objects that can overwhelm or confound an AI driving system, see the link here and the link here, for example. In a sense, the ML/DL might have been narrowly derived and either fail to recognize an OOD circumstance or perhaps equally worse treat the OOD as though it is within the confines of conventional inside-distribution occurrences that the AI was trained on. This is the classic dilemma of treating something as a false positive or a false negative and ergo having the AI take no action when it needs to act or taking devout action that is wrongful under the circumstances.
In this insightful presentation about safe robot autonomy, a keystone emphasis entails a dire need to ensure that suitable and sufficient run-time monitoring is taking place by the AI driving system to detect those irascible and often threatening out-of-distribution instances. You see, if the run-time monitoring is absent of OOD detection, all heck would potentially break loose since the chances are that the initial training of the ML/DL would not have adequately prepared the AI for coping with OOD circumstances. If the run-time monitoring is weak or inadequate when it comes to OOD detection, the AI might be driving blind or cross-eyed as it were, not ascertaining that a boundary breaker is in its midst.
A crucial first step involves the altogether fundamental question of being able to define what constitutes being out-of-distribution. Believe it or not, this is not quite as easy as you might so assume.
Imagine that a self-driving car encounters an object or event that computationally is calculated as relatively close to the original training set but not quite on par. Is this an encountered anomaly or is it just perchance at the far reaches of the expected set?
This research depicts a model that can be used for OOD detection, called Sketching Curvature for OOD Detection or SCOD. The overall idea is to equip the pre-training of the ML with a healthy dose of epistemic uncertainty. In essence, we want to carefully consider the tradeoff between the fraction of out-of-distribution that has been correctly flagged as indeed OOD (referred to as TPR, True Positive Rate), versus the fraction of in-distribution that is incorrectly flagged as being OOD when it is not, in fact, OOD (referred to as FPR, False Positive Rate).
Ongoing and future research posited includes classifying the severity of OOD anomalies, causal explanations that can be associated with anomalies, run-time monitor optimizations to contend with OOD instances, etc., and the application of SCOD to additional settings.
Use this link here for info about the Stanford Autonomous Systems Lab (ASL).
Use this link here for info about the Stanford Center for Automotive Research (CARS).
For some of my prior coverage discussing the Stanford Center for Automotive Research, see the link here.
Presentation by Dr. Mac Schwager
Dr. Mac Schwager is an Associate Professor of Aeronautics and Astronautics at Stanford University and Director of the Stanford Multi-Robot Systems Lab (MSL)
Here’s my brief recap and erstwhile thoughts about this talk.
There are various ways of establishing a geometric representation of scenes or images. Some developers make use of point clouds, voxel grids, meshes, and the like. When devising an autonomous system such as an autonomous vehicle or other autonomous robots, you’d better make your choice wisely since otherwise the whole kit and kaboodle can be stinted. You want a representation that will aptly capture the nuances of the imagery, and that is fast, reliable, flexible, and proffers other notable advantages.
The use of artificial neural networks (ANNs) has gained a lot of traction as a means of geometric representation. An especially promising approach to leveraging ANNs is known as a neural radiance field or NeRF method.
Let’s take a look at a handy originating definition of what NeRF consists of: “Our method optimizes a deep fully-connected neural network without any convolutional layers (often referred to as a multilayer perceptron or MLP) to represent this function by regressing from a single 5D coordinate to a single volume density and view-dependent RGB color. To render this neural radiance field (NeRF) from a particular viewpoint we: 1) march camera rays through the scene to generate a sampled set of 3D points, 2) use those points and their corresponding 2D viewing directions as input to the neural network to produce an output set of colors and densities, and 3) use classical volume rendering techniques to accumulate those colors and densities into a 2D image. Because this process is naturally differentiable, we can use gradient descent to optimize this model by minimizing the error between each observed image and the corresponding views rendered from our representation (as stated in the August 2020 paper entitled NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis by co-authors Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng).
In this fascinating talk about NeRF and fostering advances in robotic autonomy, there are two questions directly posed:
The presented answers are that yes, based on initial research efforts, it does appear that NeRF can indeed be used for those proposed uses.
Examples showcased include navigational uses such as via the efforts of aerial drones, grasp planning uses such as a robotic hand attempting to grasp a coffee mug, and differentiable simulation uses including a dynamics-augmented neural object (DANO) formulation. Various team members that participated in this research were also listed and acknowledged for their respective contributions to these ongoing efforts.
Use this link here for info about the Stanford Multi-Robot Systems Lab (MSL).
Presentation by Dr. Clark Barrett, Professor (Research) of Computer Science, Stanford University
Here’s my brief recap and erstwhile thoughts about this research.
When using Machine Learning (ML) and Deep Learning (DL), an important consideration is the all-told robustness of the resulting ML/DL system. AI developers might inadvertently make assumptions about the training dataset that ultimately gets undermined once the AI is put into real-world use.
For example, a demonstrative distributional shift can occur at run-time that catches the AI off-guard. A simple use case might be an image analyzing AI ML/DL system that though originally trained on clear-cut images later on gets confounded when encountering images at run-time that are blurry, poorly lighted, and contain other distributional shifts that were not encompassed in the initial dataset.
Integral to doing proper computational verification for ML/DL consists of devising specifications that are going to suitably hold up regarding the ML/DL behavior in realistic deployment settings. Having specifications that are perhaps lazily easy for ML/DL experimental purposes is well below the harsher and more demanding needs for AI that will be deployed on our roadways via autonomous vehicles and self-driving cars, driving along city streets and tasked with life-or-death computational decisions.
Key findings and contributions of this work per the researcher’s statements are:
For additional details, see the associated paper entitled Toward Certified Robustness Against Real-World Distribution Shifts, June 2022, by co-authors Haoze Wu, Teruhiro Tagomori, Alexandar Robey, Fengjun Yang, Nikolai Matni, George Pappas, Hamed Hassani, Corina Pasareanu, and Clark Barrett.
Presentation by Daniel Zhang, Policy Research Manager, Stanford Institute for Human-Centered Artificial Intelligence (HAI), Stanford University
Here’s my brief recap and erstwhile thoughts about this research.
Each year, the world-renowned Stanford Institute for Human-Centered AI (HAI) at Stanford University prepares and releases a widely read and eagerly awaited “annual report” about the global status of AI, known as the AI Index. The latest AI Index is the fifth edition and was unveiled earlier this year, thus referred to as AI Index 2022.
As officially stated: “The annual report tracks, collates, distills, and visualizes data relating to artificial intelligence, enabling decision-makers to take meaningful action to advance AI responsibly and ethically with humans in mind. The 2022 AI Index report measures and evaluates the rapid rate of AI advancement from research and development to technical performance and ethics, the economy and education, AI policy and governance, and more. The latest edition includes data from a broad set of academic, private, and non-profit organizations as well as more self-collected data and original analysis than any previous editions” (per the HAI website; note that the AI Index 2022 is available as a downloadable free PDF at the link here).
The listed top takeaways consisted of:
There are about 230 pages of jampacked information and insights in the AI Index 2022 covering the status of AI today and where it might be headed. Prominent news media and other sources often quote the given stats or other notable facts and figures contained in Stanford’s HAI annual AI Index.
Presentation by Dr. Jan Leike, Head of Alignment, OpenAI
Here’s my brief recap and erstwhile thoughts about this talk.
Large Language Models (LLM) such as GPT-3 have emerged as important indicators of advances in AI, yet they also have spurred debate and at times heated controversy over how far they can go and whether we might misleadingly or mistakenly believe that they can do more than they really can. See my ongoing and extensive coverage on such matters and particularly in the context of AI Ethics at the link here and the link here, just to name a few.
In this perceptive talk, there are three major points covered:
As a handy example of a readily apparent alignment problem, consider giving GPT-3 the task of writing a recipe that uses ingredients consisting of avocados, onions, and limes. If you gave the same task to a human, the odds are that you would get a reasonably sensible answer, assuming that the person was of a sound mind and willing to undertake the task seriously.
Per this presentation about LLMs limitations, the range of replies showcased via the use of GPT-3 varied based on minor variants of how the question was asked. In one response, GPT-3 seemed to dodge the question by indicating that a recipe was available but that it might not be any good. Another response by GPT-3 provided some quasi-babble such as “Easy bibimbap of spring chrysanthemum greens.” Via InstructGPT a reply appeared to be nearly on target, providing a list of instructions such as “In a medium bowl, combine diced avocado, red onion, and lime juice” and then proceeded to recommend additional cooking steps to be performed.
The crux here is the alignment considerations.
How does the LLM align with or fail to align to the stated request of a human making an inquiry?
If the human is seriously seeking a reasonable answer, the LLM should attempt to provide a reasonable answer. Realize that a human answering the recipe question might also spout babble, though at least we might expect the person to let us know that they don’t really know the answer and are merely scrambling to respond. We naturally might expect or hope that an LLM would do likewise, namely alert us that the answer is uncertain or a mishmash or entirely fanciful.
As I’ve exhorted many times in my column, an LLM ought to “know its limitations” (borrowing the famous or infamous catchphrase).
Trying to push LLMs forward toward better human alignment is not going to be easy. AI developers and AI researchers are burning the night oil to make progress on this assuredly hard problem. Per the talk, an important realization is that LLMs can be used to accelerate the AI and human alignment aspiration. We can use LLMs as a tool for these efforts. The research outlined a suggested approach consisting of these main steps: (1) Perfecting RL or Reinforcement Learning from human feedback, (2) AI-assisted human feedback, and (3) Automating alignment research.
Presentation by James “Jerry” Lopez, Autonomy Safety and Safety Research Leader, Motional
Here’s my brief recap and erstwhile thoughts about this talk.
As avid followers of my coverage regarding autonomous vehicles and self-driving cars are well aware, I am a vociferous advocate for applying AI safety precepts and methods to the design, development, and deployment of AI-driven vehicles. See for example the link here and the link here of my enduring exhortations and analyses.
We must keep AI safety at the highest of priorities and the topmost of minds.
This talk covered a wide array of important points about AI safety, especially in a self-driving car context (the company, Motional, is well-known in the industry and consists of a joint venture between Hyundai Motor Group and Aptiv, for which the firm name is said to be a mashup of the words “motion” and “emotional” serving as a mixture intertwining automotive movement and valuation of human respect).
The presentation noted several key difficulties with today’s AI in general and likewise in particular to self-driving cars, such as:
Another consideration is the incorporation of uncertainty and probabilistic conditions. The asserted “four horsemen” of uncertainty were described: (1) Classification uncertainty, (2) Track uncertainty, (3) Existence uncertainty, and (4) Multi-modal uncertainty.
One of the most daunting AI safety challenges for autonomous vehicles consists of trying to devise MRMs (Minimal Risk Maneuvers). Human drivers deal with this all the time while behind the wheel of a moving car. There you are, driving along, and all of a sudden a roadway emergency or other potential calamity starts to arise. How do you respond? We expect humans to remain calm, think mindfully about the problem at hand, and make a judicious choice of how to handle the car and either avoid an imminent car crash or seek to minimize adverse outcomes.
Getting AI to do the same is tough to do.
An AI driving system has to first detect that a hazardous situation is brewing. This can be a challenge in and of itself. Once the situation is discovered, the variety of “solving” maneuvers must be computed. Out of those, a computational determination needs to be made as to the “best” selection to implement at the moment at hand. All of this is steeped in uncertainties, along with potential unknowns that loom gravely over which action ought to be performed.
AI safety in some contexts can be relatively simple and mundane, while in the case of self-driving cars and autonomous vehicles there is a decidedly life-or-death paramount vitality for ensuring that AI safety gets integrally woven into AI driving systems.
Presentation by Peter Henderson, JD/Ph.D. Candidate at Stanford University
Here’s my brief recap and erstwhile thoughts about this talk.
Readers of my columns are familiar with my ongoing clamor that AI and the law are integral dance partners. As I’ve repeatedly mentioned, there is a two-sided coin intertwining AI and the law. AI can be applied to law, doing so hopefully to the benefit of society all told. Meanwhile, on the other side of the coin, the law is increasingly being applied to AI, such as the proposed EU AI Act (AIA) and the draft USA Algorithmic Accountability Act (AAA). For my extensive coverage of AI and law, see the link here and the link here, for example.
In this talk, a similar dual-focus is undertaken, specifically with respect to AI safety.
You see, we ought to be wisely considering how we can enact AI safety precepts and capabilities into the governmental use of AI applications. Allowing governments to willy-nilly adopt AI and then trust or assume that this will be done in a safe and sensible manner is not a very hearty assumption (see my coverage at the link here). Indeed, it could be a disastrous assumption. At the same time, we should be urging lawmakers to sensibly put in place laws about AI that will incorporate and ensure some reasonable semblance of AI safety, doing so as a hardnosed legally required expectation for those devising and deploying AI.
Two postulated rules of thumb that are explored in the presentation include:
As a closing comment that is well worth emphasizing over and over again, the talk stated that we need to embrace decisively both a technical and a regulatory law mindset to make AI Safety well-formed.
Presentation by Dr. Mykel Kochenderfer, Associate Professor of Aeronautics and Astronautics at Stanford University and Director of the Stanford Intelligent Systems Laboratory (SISL)
Here’s my brief recap and erstwhile thoughts about this talk.
This talk highlighted some of the latest research underway by the Stanford Intelligent Systems Laboratory (SISL), a groundbreaking and extraordinarily innovative research group that is at the forefront of exploring advanced algorithms and analytical methods for the design of robust decision-making systems. I can highly recommend that you consider attending their seminars and read their research papers, a well-worth instructive and engaging means to be aware of the state-of-the-art in intelligent systems (I avidly do so).
Use this link here for official info about SISL.
The particular areas of interest to SISL consist of intelligent systems for such realms as Air Traffic Control (ATC), uncrewed aircraft, and other aerospace applications wherein decisions must be made in complex and uncertain, dynamic environments, meanwhile seeking to maintain sufficient safety and efficacious efficiency. In brief, robust computational methods for deriving optimal decision strategies from high-dimensional, probabilistic problem representations are at the core of their endeavors.
At the opening of the presentation, three key desirable properties associated with safety-critical autonomous systems were described:
In the category of Accurate Modeling, these research efforts were briefly outlined (listed here by the title of the efforts):
In the category of Self-Assessment, these research efforts were briefly outlined (listed here by the title of the efforts):
In the category of Validation and Verification, these research efforts were briefly outlined (listed here by the title of the efforts):
In addition, a brief look at the contents of the impressive book Algorithms For Decision Making by Mykel Kochenderfer, Tim Wheeler, and Kyle Wray was explored (for more info about the book and a free electronic PDF download, see the link here).
Future research projects either underway or being envisioned include efforts on explainability or XAI (explainable AI), out-of-distribution (OOD) analyses, more hybridization of sampling-based and formal methods for validation, large-scale planning, AI and society, and other projects including collaborations with other universities and industrial partners.
Presentation by Dr. Dorsa Sadigh, Assistant Professor of Computer Science and of Electrical Engineering at Stanford University
Here’s my brief recap and erstwhile thoughts about this research.
Let’s start with a handy scenario about the difficulties that can arise when devising and using AI.
Consider the task of stacking cups. The tricky part is that you aren’t stacking the cups entirely by yourself. A robot is going to work with you on this task. You and the robot are supposed to work together as a team.
If the AI underlying the robot is not well-devised, you are likely to encounter all sorts of problems with what otherwise would seem to be an extremely easy task. You put one cup on top of another and then provide the robot a chance to place yet another cup on top of those two cups. The AI selects an available cup and tries gingerly to place it atop the other two. Sadly, the cup chosen is overly heavy (bad choice) and causes the entire stack to fall to the floor.
Imagine your consternation.
The robot is not being very helpful.
You might be tempted to forbid the robot from continuing to stack cups with you. But, assume that you ultimately do need to make use of the robot. The question arises as to whether the AI is able to figure out the cup stacking process, doing so partially by trial and error but also as a means of discerning what you are doing when stacking the cups. The AI can potentially “learn” from the way in which the task is being carried out and how the human is performing the task. Furthermore, the AI could possibly ascertain that there are generalizable ways of stacking the cups, out of which you the human here have chosen a particular means of doing so. In that case, the AI might seek to tailor its cup stacking efforts to your particular preferences and style (don’t we all have our own cup stacking predilections).
You could say that this is a task involving an assistive robot.
Interactions take place between the human and the assistive robot. The goal here is to devise the AI such that it can essentially learn from the task, learn from the human, and learn how to perform the task in a properly assistive manner. Just as we wanted to ensure that the human worked with the robot, we don’t want the robot to somehow arrive at a computational posture that will simply circumvent the human and do the cup stacking on its own. They must collaborate.
The research taking place is known as the ILIAD initiative and has this overall stated mission: “Our mission is to develop theoretical foundations for human-robot and human-AI interaction. Our group is focused on: 1) Formalizing interaction and developing new learning and control algorithms for interactive systems inspired by tools and techniques from game theory, cognitive science, optimization, and representation learning, and 2) Developing practical robotics algorithms that enable robots to safely and seamlessly coordinate, collaborate, compete, or influence humans (per the Stanford ILIAD website at the link here).
Some of the key questions being pursued as part of the focus on learning from interactions (there are other areas of focus too) include:
You have now been taken on a bit of a journey into the realm of AI safety.
All stakeholders including AI developers, business and governmental leaders, researchers, ethicists, lawmakers, and others have a demonstrative stake in the direction and acceptance of AI safety. The more AI that gets flung into society, the more we are taking on heightened risks due to the existent lack of awareness about AI safety and the haphazard and at times backward ways in which AI safety is being devised in contemporary widespread AI.
A proverb that some trace to the novelist Samuel Lover in one of his books published in 1837, and which has forever become an indelible presence even today, serves as a fitting final comment for now.
What was that famous line?
It is better to be safe than sorry.
Enough said, for now.