Musk may have put a top-notch hardware implant in a pig — but he didn’t mention plans for clinical trials on humans during the event earlier this year, which some expected. BIOS, however, is about to embark on human trials next year. The startup aims to treat diseases, for which we don’t currently have effective drugs, by rewiring the brain. Part of the problem with conditions such as heart failure, arthritis, diabetes and Crohn’s disease is that the signals between the brain and diseased organs are failing. By fixing this could dramatically improve the health and wellbeing of patients. But being able to understand the complex neural codes that connect the brain with organs — and to rewire them — is more complex than what Neuralink has been able to show so far. “We are a bit like Linux if Elon Musk is Microsoft,” the cofounder Emil Hewage tells Sifted. Like Neuralink, BIOS has developed its own implant but is focusing on the data that is extracted from it more instead of making the hardware less clunky. The company was founded by the computer neuroscientist Hewage and the bioengineer Oliver Armitage in Cambridge in 2015 as a way to commercialise all the science that had been achieved in the field in the last 20 years.
To some degree, insurers are making the problem worse. In many ransomware attacks, insurers determine that paying the ransom is the least expensive way for their policyholders to recover. Such payouts, however, also keep extortion rackets in business and attacking other companies. If significant and widespread events become more common, it could have a dramatic impact on the cyber insurance industry, says Chris Kennedy, CISO at AttackIQ, a security-validation firm. "These black-swan events are very costly, and insurance companies are businesses, too," he says. "If we are going to see more and more of these black-swan events, the question is how can insurance companies afford to underwrite these policies? Just like the beaches in Florida or the flooding in Texas — where you can't get insurance anymore — if ransomware continues to be as rampant as it is, cyber insurers are going to back away from covering the damages." The impact of NotPetya on shipping giant A.P. Moller Maersk is a prime example of the risk. The company claimed more than $300 million in damages when the NotPetya worm shut down systems across the company's offices. However, the most significant threat to Maersk's business was that the worm infected and seemingly wiped all of the company's 150-plus domain controllers.
Aside from differences in the languages themselves, there’s also the development environment to consider. It used to be that .NET developers generally used Visual Studio, and anyone building frontend apps with something like React would use a different tool. These days tools like Visual Studio Code have successfully brought the various camps together to varying degrees. Saying that, if your team is comfortable and familiar with one particular set of tools, it may make sense to keep them there. It’s no small undertaking to switch from one coding environment to another. Over time we tend to get used to the tools/configurations we use all day every day: Shortcuts, extensions, themes all help to keep us on track. It’s not an impossible mountain to climb, to switch from one tool or editor to another, but doing so is likely to slow everything down, at least in the short term. If IDEs and text editors are an important factor when it comes to development, how you get your code to production is just as (if not more) important! Angular, React and Blazor all have their own processes for deployment. In general, it’s a matter of running the correct script to package up your apps before deploying them to some form of host.
The initial accomplishments reaped in the use of our first Obstacle Board were great. However, over time we learned that maintaining the same approach was quite challenging. This particular team actually stopped using the board 3 ½ months after starting the experiment. Reflecting on this stoppage, I would definitely consider changing a few aspects of how we used the board at that time to help it better integrate itself as a permanent feature of our practice, and to educate others hoping to follow in our footsteps. Firstly, while we could see from the previous burndown illustration that the proportion of completed to committed stories is veering towards 100%, we didn’t reach that point within the experiment timeframe. The most likely reason for this was that we didn’t get that initial work balance of stories to obstacles right. Just like teams will use their prior sprint velocity, or perhaps an average in their sprint planning activities, so too should we have tried to better track the time taken on obstacles to adjust that ratio. Secondly, while in this experiment we fixed the definition of an obstacle to be these data validation issues, this proved to impact the longevity of the board usage. As any team grows and develops over time, what causes them to slow down evolves. If you do not revisit the causes of what slows you down regularly, you may not think of those new blockers as obstacles.
Digital transformation projects have traditionally been grounded in the adoption of new business technologies that promise to unlock innovation by streamlining projects and enhancing workflows, but they typically work from the top down in a broad vision. This type of innovation is incapable of keeping up with drastically changing business needs, nor can it compete with today’s rapidly evolving digital landscape where every executive leader is working overtime to stay ahead of market volatility. Those at the top must focus their attention on high-level initiatives that grow and unite the business. This means that business leaders must shift away from a one-dimensional approach to digital transformation in favor of a modern, hybrid model -- one that engages workers on the frontlines of the business to collaboratively identify lapses in business processes and develop innovative solutions. These are the folks that are closest to the actual work and are best positioned to identify and remediate the problems they face day-to-day. The value these workers can bring to innovation initiatives can be ground-breaking for the business, and in most companies, this potential remains largely untapped.
Now is the time to make empiricism new again. Slow down, bring our community back to three pillars at the heart of agility: ... Transparency - Continuous attention to revealing the system around us, and not the defined processes and procedures. Specific focus and attention to revealing the human and relationship systems within teams and organizations and how they work together to create or impede the delivery of value; Inspection - Two perspectives on inspection are needed for the transition to the future. The first starts with self - how each individual approaches their own personal development & professionalism. Second, systemic development & professionalism - how teams, communities, and cultures collectively pursue mission-driven work. Inquiry should balance ones that are deep and exploratory with others guided by the pursuit of outcome-oriented ways for creating value with customers and constituents. Adaptation - The cycle to break with adaptation is change-for-change-sake. There must be a courageous dismantling of self-limiting beliefs, engrained patterns of behavior, and historical non-value-add metrics. Dismantling these creates space to adapt based on the results of inspection, experimentation, and evaluation of evidence that indicates where and how adjustments should be made.
Neuralink is an ambitious neurotechnology company that’s aiming to upgrade nature’s most complex organ – the human brain. Founded by serial entrepreneur Elon Musk, it hopes to surgically implant tiny devices deep inside the skull, offering the potential to treat brain disorders and other medical problems, and give us the power to interact with and control machines using our minds. The idea currently falls quite firmly in the realm of sci-fi and is either utopian or dystopian, depending on who you talk to. Musk refers to it as a “Fitbit in your skull, with tiny wires”, but this is no easy install. The company would need to insert 3,072 electrodes connected to 96 thin, flexible threads into your brain. ... The human brain has 86 billion neurons, which send and receive information through electric signals via synapses. With Neuralink, each individual thread of the device will be connected in the brain, allowing it to monitor the activity of 1,000 brain neurons. Although that sounds like a small sample, amplified signals are recorded and interpreted as digital instructions, and information is sent back to the brain to stimulate electrical spikes. Data in the prototypes has been transmitted via a wired USB-C connection but the goal has been to create a wireless system.
Despite its gaining popularity, as the data from the joint study found, users continue to have difficulty learning to use the framework. There are two fundamental challenges, Sarukkai said. "A lot of tools didn't have the ability to support it. Enterprises who don't have these products end up doing it manually which they means they aren't fully able to adopt the Mitre ATT&CK framework because they are getting inundated with instances and because they don't have the tooling they need to be effective. That's the biggest reason," he said. The second problem, Sarukkai said, is that organizations want to use ATT&CK to automate remediation and help alleviate the workload on SOC analysts. But such use requires a level of maturity with ATT&CK, and the report found that just 19% of respondents have reached that maturity level. The biggest challenge, according to Pennington, is people being overwhelmed. "We recognize that. ATT&CK for Enterprise, the main knowledge base people are using, is 156 high-level behaviors as of right now. And so, if an organization is going in and trying to just go across and immediately in one pass figure out what their stance is against 156 behaviors, they'll be overwhelmed, and we've seen that," he said.
Pushed by the pandemic, many businesses have no choice but to rely on their digital channels, he says. As organizations focus on building up reliability and put preventive measures in place, the effort becomes data intensive, Gilfix says. “People have to sift through logs that come from applications and network devices. They have to set up monitoring and alert tools,” he says. “They have to leverage all these various forms of data to figure out where the application is working, and they have to have mature abilities to build a development staging pipeline.” That means testing the applications, simulating real world needs, and moving change management into product, Gilfix says. Finding skilled professionals capable of performing those tasks quickly with large-scale applications is a challenge. This is where AIOps, the application of artificial intelligence to make sense of that data for DevOps, comes into play, he says. “Issues can be resolved quicker,” Gilfix says. “You can pinpoint similar issues in your applications and fix them preventatively. You can leverage AI to ensure, in a decentralized manner, you’re compliant and manage risk.” AI can also be used to avoid errors downstream in the development process.
At a global level, there is a spectrum of consumer data privacy regulations. On one end, the European Union's GDPR gives individuals complete control over their personal data and who can access it. Enterprises processing such data must have strict technical and organizational measures in place to ensure data protection principles such as de-identification practices or full anonymization. When data is being processed, it must be done for one of six lawful reasons and the data subject is able to revoke permission at any time. Although strict data management protects consumers' privacy, from an artificial intelligence point of view it inadvertently may limit access to critical data elements or reduce the size of the data set which ultimately could affect the ability to create accurate algorithms. Additionally, limited-size data sets can greatly impact progress on research developments. On the other end of the spectrum is China. With the largest population of internet users in the world, organizations can collect an enormous amount of data on customers that can be used in enterprise AI solutions. Because there are fewer restrictions about who can view and leverage personal data, Chinese data scientists are in many cases able to use the country's massive data sets as a competitive advantage in developing new AI algorithms.
Quote for the day:
"Confident and courageous leaders have no problems pointing out their own weaknesses and ignorance." -- Thom S. Rainer