Beyond “Agree to Disagree”: Why Leaders Need to Foster a Culture of Productive Disagreement and Debate
The business imperative of nurturing a culture of productive disagreement is clear. The good news is that senior leaders can play a highly influential role in this regard. By integrating the concepts of openness and healthy debate into their own and their organization’s language they can institutionalize new norms. Their actions can help to further reset the rules of engagement by serving as a model for employees to follow. ... Leaders should incorporate the concept of productive debate into corporate value statements and the way they address colleagues, employees, and shareholders. Michelin, for example, built debate into its value statement. One of its organizational values is “respect for facts,” which it describes as follows: “We utilize facts to learn, honestly challenge our beliefs….” Another company that espouses debate as value is Bridgewater. Founder Ray Dalio ingrained principles and subprinciples such as “be radically open-minded” and “appreciate the art of thoughtful disagreement” in the investment management company’s culture.
Because I believe that anyone that wants to be a CIO or a CTO, particularly in the way that the industry is progressing, you need to understand technology. So, staying close to the technology and curious and wanting to solve those problems has helped me. But there's another part to it, too. In every one of my roles, there have been times when I've seen something that wasn't necessarily working and I had ideas and wanted to help, but it might’ve been outside of my responsibility. I've always leaned in to help, even though I knew that it was going to help someone else in the organization, because it was the right thing to do and it helped the company, it helped other people. So, it ended up building stronger relationships, but also building my skillset. I think that's been a part of my rise too, and it's something that's just incredibly powerful from a cultural perspective. That’s something that I love here. Everybody is in it together to work that way. But I also think that it just speaks volumes about an individual, and people gravitate to want to work with people that operate that way.
According to the researchers, this technique for generating stable qubits could have massive implications for the entire field of quantum computing, but especially for scalability and noise-reduction: At this stage, our system faces mostly technical limitations, such as optical losses, finite cooperativity and imperfect Raman pulses. Even modest improvements in these respects would put us within reach of loss and fault tolerance thresholds for quantum error correction. It’ll take some time to see how well this experimental generation of qubits translates into an actual computation device, but there’s plenty of reason to be optimistic. There are numerous different methods by which qubits can be made, and each lends to its own unique machine architecture. The upside here is that the scientists were able to generate their results with a single atom. This indicates that the technique would be useful outside of computing. If, for example, it could be developed into a two-atom system, it could lead to a novel method for secure quantum communication.
When choosing a web data collection platform or network, it’s important that security professionals use a compliance-driven service provider to safeguard the integrity of their network and operations. Compliant data collection networks ensure that security operators have a safe and suitable environment in which to perform their work without being compromised by potential bad actors using the same network or proxy infrastructure. These data providers institute extensive and multifaceted compliance processes that include a number of internal as well as external procedures and safeguards, such as manual reviews and third-party audits, to identify non-compliant active patterns and ensure that all use of the network follows the overall compliance guidelines. This of course also includes abiding by the data gathering guidelines established by international regulators, such as the European Union and the US State of California, as well as enforcing others who follow public web scraping best practices for compliant and reliable web data scraping or collection.
It’s not like TensorFlow has stood still for all that time. TensorFlow 1.x was all about building static graphs in a very un-Python manner, but with the TensorFlow 2.x line, you can also build models using the “eager” mode for immediate evaluation of operations, making things feel a lot more like PyTorch. At the high level, TensorFlow gives you Keras for easier development, and at the low-level, it gives you the XLA optimizing compiler for speed. XLA works wonders for increasing performance on GPUs, and it’s the primary method of tapping the power of Google’s TPUs (Tensor Processing Units), which deliver unparalleled performance for training models at massive scales. Then there are all the things that TensorFlow has been doing well for years. Do you need to serve models in a well-defined and repeatable manner on a mature platform? TensorFlow Serving is there for you. Do you need to retarget your model deployments for the web, or for low-power compute such as smartphones, or for resource-constrained devices like IoT things? TensorFlow.js and TensorFlow Lite are both very mature at this point.
Energy harvesting is nothing new, with solar power being one of the most famous examples. Solar energy works well for powering parking meters, but if we’re going to bring online the packaging and containers that are at the heart of our supply chains—things that are indoors and stacked on top of each other—we need another solution. The technology that gives mundane things like transporting cash registers both their intelligence and energy-harvesting power are small, inexpensive, brand-size computers printed as stickers and affixed to cash registers, sweater tags, vaccine vials, or other items racing in the global supply chain. These sticker tags, called IoT Pixels, include an ARM processor, a Bluetooth radio, sensors, and a security module — basically a complete system-on-a-chip (SoC). All that remains is to power this tiny SoC in the most efficient and economical way possible. It turns out that as wireless networks permeate our lives and radio frequency (RF) activity is everywhere, the prospect of recycling that RF activity into energy is the most practical and ubiquitous solution.
CoAuthor is based on GPT-3, one of the recent large language models from OpenAI, trained on a massive collection of already-written text on the internet. It would be a tall order to think a model based on existing text might be capable of creating something original, but Lee and her collaborators wanted to see how it can nudge writers to deviate from their routines—to go beyond their comfort zone (e.g., vocabularies that they use daily)—to write something that they would not have written otherwise. They also wanted to understand the impact such collaborations have on a writer’s personal sense of accomplishment and ownership. “We want to see if AI can help humans achieve the intangible qualities of great writing,” Lee says. Machines are good at doing search and retrieval and spotting connections. Humans are good at spotting creativity. If you think this article is written well, it is because of the human author, not in spite of it. ... The goal, Lee says, was not to build a system that can make humans write better and faster. Instead, it was to investigate the potential of recent large language models to aid in the writing process and see where they succeed and fail.
The breach itself actually happened two weeks before that, the company said, and involved attackers getting into the system where LastPass keeps the source code of its software. From there, LastPass reported, the attackers “took portions of source code and some proprietary LastPass technical information.” We didn’t write this incident up last week, because there didn’t seem to be a lot that we could add to the LastPass incident report – the crooks rifled through their proprietary source code and intellectual property, but apparently didn’t get at any customer or employee data. In other words, we saw this as a deeply embarrassing PR issue for LastPass itself, given that the whole purpose of the company’s own product is to help customers keep their online accounts to themselves, but not as an incident that directly put customers’ online accounts at risk. However, over the past weekend we’ve had several worried enquiries from readers (and we’ve seen some misleading advice on social media), so we thought we’d look at the main questions that we’ve received so far.
The FBI observed cybercriminals exploiting vulnerabilities in smart contracts that govern DeFi platforms in order to steal investors’ cryptocurrency. In a specific example, the FBI mentioned cases where hackers used a “signature verification vulnerability” to plunder $321 million from the Wormhole token bridge back in February. It also mentioned a flash loan attack that was used to trigger an exploit in the Solana DeFi protocol Nirvana in July. However, that’s just a drop in a vast ocean. According to an analysis from blockchain security firm CertiK, since the start of the year, over $1.6 billion has been exploited from the DeFi space, surpassing the total amount stolen in 2020 and 2021 combined. While the FBI admitted that “all investment involves some risk,” the agency has recommended that investors research DeFi platforms extensively before use and, when in doubt, seek advice from a licensed financial adviser. The agency said it was also very important that the platform's protocols are sound and to ensure they have had one or more code audits performed by independent auditors.
Facial recognition technology in surveillance has improved dramatically in recent years, meaning it is quite easy to track a person as they move about a city, he said. One of the privacy concerns about the power of such technology is who has access to that information and for what purpose. Ajay Mohan, principal, AI & analytics at Capgemini Americas, agreed with that assessment. “The big issue is that companies already collect a tremendous amount of personal and financial information about us [for profit-driven applications] that basically just follows you around, even if you don’t actively approve or authorize it,” Mohan said. “I can go from here to the grocery store, and then all of a sudden, they have a scan of my face, and they’re able to track it to see where I’m going.” In addition, artificial intelligence (AI) continues to push the capabilities of facial recognition systems in terms of their performance, while from an attacker perspective, there is emerging research leveraging AI to create facial “master keys,” that is, AI generation of a face that matches many different faces, through the use of what’s called Generative Adversarial Network techniques, according to Lewis.
Quote for the day:
"If you don't demonstrate leadership character, your skills and your results will be discounted, if not dismissed." -- Mark Miller