Evolution of the workforce, for example, can pose a risk. AI can replace most of the workforce, which means loss of employment for most of the labour. The uncertainty of how exactly AI would affect the economy can also be challenging for some. Since the world is getting smaller, AI would need to work by rules that stand globally, rules that allow for effective interaction all over the world. Imposition of such rules isn't at all an easy task. Regulation of AI is tricky too; with the introduction of new technologies, the older regulatory rules can easily be obsolete. The development of AI also allows for malpractices, such as hacking or AI trafficking. Built-In-Bias allows for the programmer of the AI to introduce, either intentionally or unintentionally, a Bias. An Artificial Intelligence working with a bias or learning from biased data would also produce biased results. This can give an arbitrary group, in some cases, an unfair advantage over the others, although the outcome of a biased AI being ‘unpredictable’ isn’t any less of a nuisance. “It’s really easy to give AI the wrong problem to solve.” Which, she says, can be quite destructive.
Banks recognise the importance of investing in technology to improve customer services, with AI’s potential to personalise customer experience seen as an attractive prospect. Some 77% of respondents said AI will separate the winners and the losers. Digital advisers and voice-assisted engagement channels will be the destination for a large part of AI investments, said the report. Beyond AI, there has been an increased acceptance that new technology will drive banking over the next five years, with 66% of banking executives agreeing, compared with 42% in the same survey in 2019. Almost half (45%) of the 300 senior executives questioned globally are planning to transform into digital ecosystems to improve customer experience and introduce new revenue streams. This will see a shift in the way banks develop software with an increase in use of DevOps. Most respondents (84%) agreed that DevOps will drive transformation in core banking. The report also said that the Covid-19 pandemic has accelerated digital transformation at banks.
The key here is to better understand what a specific benchmark metric is actually testing. Does the test represent as close to real-world workloads as possible? An ideal benchmark uses actual applications that a consumer would use, but short of that it could employ the same core software components of popular apps instead, to represent realistic performance expectations. And in this case, that means we need to understand what NNs these benchmark tools are testing against, and what mathematical precision and AI algorithms are being used to process workloads on them. What makes for a good AI benchmark for mobile devices is a relatively deep, nuanced subject, but the long and short of it is virtually all mobile NPUs (Neural Processing Units, or dedicated AI engines) employ either INT8 or quantized mathematical precision, or FP16 floating point precision, to make use of popular NNs like ResNet-34 or Google’s DeepLab-v3 for image classification and segmentation in apps, for example. Is that a cat or a dog? What sort of color balance should be applied in this camera shot? These are the kinds of questions the AI is trying to infer answers for from the phone’s environment, in an imaging workload example at least, though there are many others.
With organisations spending big on cloud, and not so much on keeping older on-premises kit up to date, there has been an increase in obsolete and unpatched network devices that contain software vulnerabilities, which NTT said introduces risk and exposes organisations to information security threats. The remarks were made in a report from the global giant that was based on more than 1,000 clients, covering over 800,000 network devices in five regions, across multiple industry sectors. In the report, NTT found 46.3% of organisations' network assets were ageing or obsolete. It said obsolete devices had, on average, twice as many vulnerabilities per device when compared with ageing and current ones, at 42.2 security advisories per device. It said such risk is intensified when a business does not patch a device or revisit the operating system version for the duration of its lifetime, which NTT said many do not do. "In this 'new normal' many businesses will need, if not be forced, to review their network and security architecture strategies, operating, and support models to better manage operational risk," NTT executive vice president of intelligent infrastructure Rob Lopez said, in light of more people working remotely due to the COVID-19 pandemic.
Schedule regular (if not daily) meetings to ensure issues are being addressed and strategies are being changing as needed in real-time. This team should have full business representation, including executive staff, regional leaders, and security operations representatives. Although many businesses may currently have these teams in place, it's important that proactive planning remains a top priority even as offices begin to reopen. This team, and the lessons they provide, will be crucial for any future pandemics or crises that pose a threat to business continuity, allowing employees to act faster and make informed decisions. Due to the rise of remote work and expanded attack services, phishing attacks have also seen a significant acceleration with employees being enticed by fake password management, executive updates, and GoFundMe messages. To decrease the impact of these attacks, it's important to keep employees informed of the latest threats and how they can protect themselves or seek support if they have become a victim. Employee education is essential, including training on how to lock down home routers with complex passwords and leverage data loss prevention (DLP) technologies.
It’s a DevOps world—everyone’s trying to move faster. Productivity increases, but so does the security risk. Yesterday, the best practice was to re-architect the code before it went into production on the standard operations platform chosen by IT. Today in the interest of speed, organizations are deploying applications developed on containers straight into production, managing them with Kubernetes and running them somewhere in the cloud (potentially still on-premises, but frequently on a public cloud service). In this model, both the developers and the operations team need to become more security-aware, and security must be fully integrated into the software life cycle. Many of our customers are experimenting with technologies from different vendors, running on multiple cloud providers, and even deploying applications across multiple platforms at once. This keeps your options open for either cost optimization or to utilize the stack that best fits a given need, and avoids vendor lock-in, but can be difficult on developers, particularly at the serverless level where standards are still emerging.
With data from a pre-COVID environment not matching the real world anymore, supervised algorithms are running out of examples to base their predictions on. And to make matters worse, AI systems don't flag their uncertainties to their human operator. "The AI won't tell you when it actually isn't confident about the accuracy of its prediction and needs a human to come in," said Barber. "There are many uncertainties in these systems. So it is important that the AI can alert the human when it is not confident about its decision." This is what Barber described as an "AI co-worker situation", where humans and machines would interact to make sure that gaps aren't left unfilled. In fact, it is a method within artificial intelligence that is slowly emerging as a particularly efficient one. Dubbed "active learning", it consists of establishing a teacher-learner relationship between AI systems and human operators. Instead of feeding the algorithm a huge labeled dataset, and letting it draw conclusions – often in a less-than-transparent way – active learning lets the AI system do the bulk of data labeling on its own, and crucially, ask questions when it has a doubt.
Interestingly, about three-fourths of those organizations surveyed said they have more than 76% of their employees working from up — that’s up from 25% in 2019. Still, a third of those surveyed said their organization is ill-prepared or not prepared to support remote working. Yet, 75% of businesses transitioned to remote working within 15 days. “Surprisingly, less than a third expressed cost or budget problems, demonstrating the urgency to support their business. Additionally, more than half (54%) expressed that COVID-19 has accelerated migration of users’ workflows and applications to the cloud,” stated the report. How are survey respondents securing their staff who work from home? The survey found the most common to be endpoint security, firewalls, virtual private networks and multi-factor authentication. The 2020 Remote Work-From-Home Cybersecurity Report is based on a survey of 413 security decision makers, conducted in May of this year, within multiple industries, including financial services, healthcare, manufacturing, high-tech, government and education.
If we go back to its origins, "lean" refers to the study of Toyota management practices outside of Toyota. It was started by a MIT research project in 1985, which compared the Japanese and occidental approaches to automotive manufacturing. At that time Toyota was already showing exceptional performance, and it ended up becoming the world's largest manufacturer 20 years later. What Toyota understood early on is that the western approach to industrialization, with a strong focus on processes and management by objectives, leads to employee disengagement and poor performance. An industrial operation is a very complex system, involving thousands of people for a single car, and subject to tens of thousands of daily problems. You need a skilled and creative workforce to be able to adapt to the resulting complexity. Toyota managers realized that most of these problems were the result of people's misconceptions about their work. They developed the Toyota Production System, which we now call the Thinking People System, as a comprehensive approach to developing team members by helping them study these problems in depth ...
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