Mediaboss Marketing

Generative AI has been heralded as the most profoundly impactful technological innovation since the iPhone. ChatGPT in particular has captured the worlds attention with its ability to convincingly generate whatever text the user desires. It has passed numerous standardized tests, from the Bar exam to the SATs, and its essay-writing prowess poses an existential threat to the integrity of education itself.

Other tools have shown impressive results in generating art, videos, code, and more. Not surprisingly, many predict that these generative AI tools will be widely disruptive particularly for industries like media, marketing and legal that deal with text and images. Whats less obvious is how generative AI will impact supply chains.

The truth is that text and image generation is just the beginning of what generative AI can accomplish. It can also be used to generate solutions to optimization problems that abound within supply chains.

Generally speaking, any situation where you have a wide range of possible solutions, and you want to find the best solution, can be thought of as an optimization problem. A simple example is when Google Maps tries to find the fastest possible route to your destination. While this works reliably well, for more complex optimization problems, classical computers dont have an efficient way to find the best solution and can only generate approximate solutions.

In contrast, a generative model could be trained on the best existing solutions to an optimization problem for example those obtained from classical heuristics or MIP solvers and learn what makes a good solution good. Much like how ChatGPT learns from existing text to generate new text, this generative model could then generate new solutions to the optimization problem. We call this approach Generator-Enhanced Optimization (GEO).

Potential supply chain use cases include finding more efficient shipping routes, optimizing the organization of warehouses to speed up order-picking, or selecting the best combination of suppliers, distributors and vendors. Given the complexity of most global supply chains, there is ample room for optimization and cost savings as a result.

It sounds promising, but for years quantum computing has also been touted for its ability to solve optimization problems, and yet today there is not yet a documented example of quantum computers providing an advantage for optimization. However, generative AI may be the fastest avenue to realize that quantum advantage. It may also be the first place we see a practical quantum advantage at all.

To vastly oversimplify things, generative models like those behind ChatGPT work by learning patterns in massive datasets and producing new data that conforms to these patterns. In other words, they learn to replicate the probability distributions of the training data. Quantum computers have the ability to encode and sample from complex probability distributions in a way that classical computers cannot, giving them a potential advantage in generative modeling.

How is this possible? For one, quantum entanglement can encode distant correlations within a dataset in ways that would be difficult for a classical computer to simulate. Secondly, the inherently probabilistic nature of measuring a quantum state makes quantum computers the ideal vehicle for sampling from probability distributions.

The end result is the ability to generate a more diverse range of solutions to the generative modeling task. In the context of optimization, this means quantum generative models could generate new, previously unconsidered solutions.

But theres a catch. Quantum devices are currently limited by low qubit counts and high error rates. But we dont necessarily need quantum devices. However, tensor networks, originally popularized among quantum physicists for simulating quantum states on classical computers, can be used for generative modeling today. And as quantum hardware matures, these quantum-inspired models can be translated into real quantum circuits, making them forward compatible with future quantum devices.

Tensor networks have shown particular value for optimization problems with equality constraints. An equality constraint is a condition that must be satisfied exactly for the solution to be valid. Without a way to natively encode these constraints, traditional optimizers can generate many invalid solutions, resulting in inefficient and expensive searches.

On the other hand, tensor networks can be constrained in a way that only outputs valid samples, resulting in the generation of more novel and high-quality solutions to optimization problems. And while equality constraints can worsen the performance of other quantum or quantum-inspired approaches, the opposite is true with constrained tensor networks, which deliver better computational performance at a cheaper cost for each additional equality constraint.

There are many possible applications of GEO that could make the supply chain more efficient. Below are a few examples:

Of course, supply chains can vary widely from industry to industry. You may have additional optimization use cases that are unique to your business. But across the board, generating better optimization solutions has the potential to reduce costs and speed up the supply chain. Optimization could also reduce waste and cut carbon emissions a great place to start for businesses looking for ways to reduce their carbon footprint.

How great is the potential value at stake? The only way to find out is to try. We are still in the early days of generative AI and even more so with quantum-inspired generative AI. By building and deploying generative AI applications, not only do you stand to gain a competitive advantage, but you may also make discoveries that advance the field.

Its important to reiterate that tensor networks are forward-compatible with real quantum computation. Businesses that deploy tensor networks for optimization may not only gain an advantage today, but they would also be in position to gain a potentially greater advantage as quantum hardware becomes more powerful. In other words, they will become quantum ready.

Excerpt from:
How Quantum-Enhanced Generative AI Could Help Optimize ... - Supply and Demand Chain Executive

The Israel Innovation Authority announced on Thursday the launch of a NIS 36 million (approximately $10 million) fund for human capital programs aimed at fostering expertise in quantum, AI, cleantech, foodtech, and Bio-Convergence. Through the joint governmental and private sector investment, approximately 2,000 participants will take part in 20 programs over the next two years.

The call for proposals, based on the Authority's program known as "Human Capital Fund for High-Tech", was launched in March 2023 to establish specialized human capital by offering training and qualifications in advanced technological fields. The Israel Innovation Authority, in response to the recommendations of the National Infrastructure Forum for Research and Development (Telem), will allocate a significant portion of approximately NIS 5 million ($1.4 million) to enable in-depth training for the integration of skilled professionals into the quantum computing industry.

Additionally, two pilot programs, developed in collaboration with the Ministry of Defense, have been approved to train and place Druze community members in crucial roles in artificial intelligence, AgriTech, and water technology.

From 49 program proposals received, 20 were selected to operate across the country. These programs will receive a cumulative government grant of NIS 19.8 million ($5.5 million) to facilitate the training and placement of 1,920 graduates from diverse populations.

Organizations selected to run programs include The Hebrew University, Elevation Education, The Technion, Master School, Science Abroad, Brainstorm IL, 8200 Alumni Association, AYYT Technological Applications and Data, Ramot at Tel Aviv University, Rambam Medical Center, Jordan Valley Academic College, Epsilon Climate, RACHIP VERIFICATION LTD, IDC Tech, Extra-tech, StellarAI, and Infinity Labs.

Continue reading here:
Israel Innovation Authority to invest $10 million to develop human ... - CTech

From the shocking scoop of Matt Hancocks Whatsapp messages to Boris reticence to share his messages, the use of secure messaging has become a hot topic. Although it may seem like a new trend, the recent stream of government-centred controversies are far from the first weve seen, nor are they the most costly. Last year alone, US financial institutions were fined almost two billion dollars for communicating over messaging services like WhatsApp.

Instant messaging is now a preferred means of communication, it is convenient, easily digestible and seen as secure. Whether we like it or not, the simple truth is that messaging is here to stay. We must accept this and work to update the way we view and manage messaging so as to ensure security and regulatory compliance.

As a former computer auditor for major financial institutions, turned cybersecurity expert, who now runs a company that provides secure messaging services, I can say from experience that there are three issues which businesses and governments are failing to take action on. The first I call the compliance problem, the second is the metadata problem and the third is the quantum problem. Unless we can address all three issues, we are sleepwalking into an oblivion of our own making.

As I previously mentioned, late last year major financial institutions in the US were fined almost two billion dollars for failing to prevent their employees from using non-compliant messaging services. Furthermore, there has been significant public debate over the so-called government by WhatsApp over the pandemic in the UK.

Historically, communications in finance and government were meticulously recorded and took place through official channels which had official norms. This is because, in the case of both of these areas, significant oversight is required in order to ward off malpractice and provide accountability. Many secure messaging services prevent the monitoring of communications, which is key to providing this essential compliance.

This is because with many secure messaging services the central institutions (e.g., watchdogs, governments, etc.) have no control over the messages and whether they are deleted. It also prevents regulators from accessing them in some cases. This is why banking regulators are coming down so hard when institutions use tools without an audit trail.

Fines and controversy can easily be avoided by implementing a secure chat solution without weakening any encryption, but also one that permits access to regulators in the event an investigation is required.

On average, financial institutions and governments take data security incredibly seriously. As a norm VPNs are required to access data and messaging services are centrally controlled and very secure. In finance in particular, even small data leaks can have huge implications in terms of financial and reputational losses.

For instance, imagine if metadata were obtained which showed that a CEO from a major company was messaging a CEO from another major company. Even if the content of the messages could not be seen, it may lead to inferences that an acquisition could soon take place. The speculation could have a huge impact on markets.

Many users of generic messaging platforms do not realise that this metadata can be accessed, compiled, analysed and sold on. Secure messaging providers can often access metadata like this even if end-to-end encryption prevents them from seeing the content of the message itself. Therefore, the use of these third-party messaging services can risk this metadata falling into the wrong hands and exposing all manner of sensitivities.

Thats why security and privacy sensitive industries need their own proprietary messaging services, where they control the infrastructure as well as encryption and the metadata insights that are inevitably emitted.

When a sufficiently large quantum computer is developed it will be able to access almost all encrypted data. While this may still be a few years off, encrypted data can be harvested now so that it can be decrypted later when a large enough quantum computer is developed.

Governments and major financial institutions routinely handle data which will remain sensitive for decades. This data is vulnerable to Harvest Now, Decrypt Later attacks even if it is sent over messaging services like WhatsApp that rely only on todays encryption algorithms. To be truly secure today, a messaging service needs to encrypt against both classical and quantum attacks. The latest US legislation makes this point abundantly clear by mandating federal agencies transition to post-quantum encryption.

So, what should we do?

Now that messaging is such a core part of how we communicate, we need to start a dialogue on how to do it right. At a bare minimum, we must implement solutions which are regulatorily compliant and are secure from metadata mining and quantum attack.

Organisations have long understood these principles as they relate to general networking but now is the time to apply those same tried and tested principles to how we chat.

Read this article:
The problem with secure messaging - ComputerWeekly.com

1Passwordhas begun testing a way to access the app without its namesake password, adding an option to use newer passkey technologyinstead. The change, which uses a quick biometric check to unlock and use your password vault, could help improve the widely usedpassword manager'ssecurity.

The software's primary job is to create, store, sync and autofill passwords for all the apps and websites you use. Until now, its password storage vaults have been protected by its own password (in combination with a secret key the first time you used 1Password on a new device). But now 1Password developer AgileBits has begun a private test that'll let participants unlock their password vaults with a passkey instead.

The test works on iPhones, Macs and web browsers, but it's a private beta and testers will have to set up a new account to try it. Android, Windows and Linux support will come later, as well as the ability to upgrade an existing account, the company said. 1Password aims to release the technology to everyone by the end of 2023.

1Password is CNET's current pick for best premium password manager. See how it compares to the competition in our guide to thebest password managers of 2023.

Passkey unlock for 1Password is designed to be easier to use than passwords. By default, 1Password's phone apps require you to retype your password every two weeks. But with a lower-hassle passkey authentication, you might be more inclined to keep your vaults locked, reducing risks from stolen devices.

"Unlocking 1Password with a passkey offers the best of both worlds: best-in-class security paired with maximum convenience," AgileBits said in a blog post.

Passkeys are a newer authentication technology designed to leave behind the shortcomings of password-based login. They are the top example of products developed for the "passwordless" era that tech companies are trying to gradually move us toward.

Interested in trying it out? "We recommend that folks sign up for ourpasswordless newsletter so they can be notified if and when seats in this private beta become available," the company said. If you're not a 1Password customer, you can also use passkeys on Android, iOS and web browsers with Apple and Google software that doesn't use 1Password at all.

Apple, Google and Microsoft helped develop passkeys to be as easy to use as passwords but much more secure. To use a passkey, you typically perform a face or fingerprint biometric authentication step on a device that stores the passkey. If your biometrics don't work, you can use the fall back to the device unlock procedure and type in your device's passcode.

The combination of device possession and biometric check counts as strong two-factor authentication that's more secure than a password alone or weaker two-factor authentication measures like login codes sent by text message.

In June, 1Password began testing the ability to store passkeys in its software and to sync passkeys across devices.

Password problems are abundant. Because they're hard to remember, we tend to reuse them on lots of websites and services, multiplying the ability of a hacker who obtains a password. Password managers make it easier to create strong, unique passwords, but they can be complicated to use.

Passkeys aren't without complications, though. For now, Apple can sync passkeys across Apple devices and its Safari browser, but Google syncs them across its own products. 1Password and another password manager adding passkey support, Dashlane, add extra management responsibilities.

You can set up separate passkeys to sign into the same site, though -- for example logging into Gmail with your Android phone and with Safari on your Mac. Passkey proponents are working on passkey import and export tools to ease such hassles.

Passkeys use technology called public key cryptography that's also used to secure countless online connections. Passkeys only work with the website or app they were set up with, blocking the use of fake websites to fool you into sharing your login credentials.

Google has enabled passkey login for its online services like Gmail, WorkSpace and YouTube, and its tests show passkey authentication is twice as fast as password login.

Apple, too, has embraced passkeys for signing onto iCloud and other Apple ID-based accounts with the upcoming iOS 17 and MacOS Sonoma.

See the original post:
1Password Tests Passkey Login to Unlock Your Password Vault - CNET

Quantum computers could beat classical ones at answering practical questions within two years, a new experiment from IBM computers shows. The demonstration hints that true quantum supremacy, in which quantum computers overtake classical digital ones, could be here surprisingly soon.

"These machines are coming," Sabrina Maniscalco, CEO of Helsinki-based quantum-computing startup Algorithmiq, told Nature News.

In the new study, described Wednesday (June 14) in the journal Nature, scientists used IBM's quantum computer, known as Eagle, to simulate the magnetic properties of a real material faster than a classical computer could. It achieved this feat because it used a special error-mitigating process that compensated for noise, a fundamental weakness of quantum computers.

Traditional silicon-chip-based computers rely on "bits" that can take just one of two values: 0 or 1.

By contrast, quantum computers employ quantum bits, or qubits, that can take on many states at once. Qubits rely on quantum phenomena such as superposition, in which a particle can exist in multiple states simultaneously, and on quantum entanglement, in which the states of distant particles can be linked so that changing one instantaneously changes the other. In theory, this allows qubits to make calculations much faster, and in parallel, that digital bits would do slowly and in sequence.

But historically, quantum computers have had an Achilles' heel: The quantum states of qubits are incredibly delicate, and even the tiniest disruption from the outside environment can mess with their states and thereby the information they carry forever. That makes quantum computers very error-prone or "noisy."

In the new proof-of-principle experiment, the 127-qubit Eagle supercomputer, which uses qubits built on superconducting circuits, calculated the complete magnetic state of a two-dimensional solid. The researchers then carefully measured the noise produced by each of the qubits. It turned out that certain factors, such as defects in the supercomputing material, could reliably predict the noise generated in each qubit. The team then used these predictions to model what the results would have looked like without that noise, Nature News reported.

Claims of quantum supremacy have surfaced before: In 2019, Google scientists claimed that the company's quantum computer, known as Sycamore, had solved a problem in 200 seconds that an ordinary computer would take 10,000 years to crack. But the problem it solved essentially spitting out a huge list of random numbers and then checking their accuracy, had no practical use.

By contrast, the new IBM demonstration applies to a real albeit highly simplified physical problem.

"It makes you optimistic that this will work in other systems and more complicated algorithms," John Martinis, a physicist at the University of California, Santa Barbara, who achieved the 2019 Google result, told Nature News.

You can read more about the quantum computing milestone at Nature News.

Excerpt from:
Quantum computers could overtake classical ones within 2 years, IBM 'benchmark' experiment shows - Space.com