Never summon a power you can’t control
Traditionally, the term “AI” has been used as an acronym for artificial
intelligence. But it is perhaps better to think of it as an acronym for alien
intelligence. As AI evolves, it becomes less artificial (in the sense of
depending on human designs) and more alien. Many people try to measure and even
define AI using the metric of “human-level intelligence”, and there is a lively
debate about when we can expect AI to reach it. This metric is deeply
misleading. It is like defining and evaluating planes through the metric of
“bird-level flight”. AI isn’t progressing towards human-level intelligence. It
is evolving an alien type of intelligence. Even at the present moment, in the
embryonic stage of the AI revolution, computers already make decisions about us
– whether to give us a mortgage, to hire us for a job, to send us to prison.
Meanwhile, generative AIs like GPT-4 already create new poems, stories and
images. This trend will only increase and accelerate, making it more difficult
to understand our own lives. Can we trust computer algorithms to make wise
decisions and create a better world? That’s a much bigger gamble than trusting
an enchanted broom to fetch wate
Artificial Intelligence: To regulate or not is no longer the question
First, existing laws have been amended to support the use of AI, thereby
enabling the economy to benefit from broader AI adoption. The Copyright Act
2021, for example, has been amended to clarify that copyrighted material may be
used for machine learning provided that the model developer had lawful access to
the data. Amendments to the Personal Data Protection Act (PDPA) 2012 enabled the
re-use of personal data to support research and business improvement, after
model development using anonymised data proved to be inadequate. Detecting
fraud, preserving the integrity of systems and ensuring physical security of
premises are also recognised as legitimate interests for using personal data in
AI systems. Second, regulatory guidance has been issued on how existing
regulations that protect consumers will also apply to AI systems. The Personal
Data Protection Commission has issued a set of advisory guidelines on how the
PDPA 2012 will apply at different stages of model development and deployment
whenever personal data is used. It also clarifies the level of transparency
expected from organisations deploying AI systems and how they may disclose
relevant information to boost consumer trust and confidence.
When You're Building The Future The Past Is No Longer A Guide
Artificial Intelligence (AI) definitely has its place. But when it comes to
these specific industrial and manufacturing challenges, it tends to be
fundamental engineering and physics that generate the answers – number crunching
and data processing in the extreme. That, in turn, means that the engineers
working to deliver more detailed test results, more realistic prototypes, and
run ever more fine-grained simulations turn to some of the most powerful
high-performance computing systems to power their workloads. What might have
counted as a system capable of High Performance Computing (HPC) a decade, or
even a few years ago, can quickly run out of steam. Computational fluid dynamics
(CFD) applications often use thousands of CPU cores, points out Gardinalli. But
it’s not purely a question of throwing raw power – and dollars – at the issue.
The real conundrum is how to map to a wide range of different domains which all
require different underlying infrastructure. Finite element analysis (FEA), for
example, focuses on working out how materials and structures will act under
stress. It’s therefore critical to public infrastructure as well as to vehicle
design and crash simulation.
Top companies ground Microsoft Copilot over data governance concerns
Asked how many had grounded a Copilot implementation, Berkowitz said it was
about half of them. Companies, he said, were turning off Copilot software or
severely restricting its use. "Now, it's not an unsolvable problem," he added.
"But you've got to have clean data and you've got to have clean security in
order to get these systems to really work the way you anticipate. It's more than
just flipping the switch." While AI software also has specific security
concerns, Berkowitz said the issues he was hearing about had more to do with
internal employee access to information that shouldn't be available to them.
Asked whether the situation is similar to the IT security challenge 15
years ago when Google introduced its Search Appliance to index corporate
documents and make them available to employees, Berkowitz said: "It's exactly
that." Companies like Fast and Attivio, where Berkowitz once worked, were among
those that solved the enterprise search security problem by tying file
authorization rights to search results. So how can companies make Copilots and
related AI software work? "The biggest thing is observability and not from a
data quality viewpoint, but from a realization viewpoint," said
Berkowitz.
Five incorrect assumptions about ISO 27001
We wish there were such a thing as an impenetrable cyber barrier. Unfortunately, there isn’t—not even at the highest levels. For any IT system to be effective, information must be sent and received from external sources. These days, vast amounts of data get copied and transferred every second, moving around the world at lightspeed. As a result, there are always multiple potential access points for criminals to get in. ISO 27001 – and any good cybersecurity strategy – can’t offer 100% protection against cyber threats. However, they can significantly mitigate the risks associated with these attacks. A correctly applied ISMS will make you more likely to keep any malware or bad actors out. ... ISO 27001 isn’t a one-time thing. Unfortunately, nothing is in information security – or business in general. The initial implementation is the most time-consuming aspect and may require the most significant financial investment. But once it’s in place, there’s no time to sit back and relax. Your staff will immediately switch focus to using pre-agreed KPIs to analyse your ISMS’s effectiveness, suggesting and making strategic adjustments as relevant.
How we’re using ‘chaos engineering’ to make cloud computing less vulnerable to cyber attacks
Chaos engineering involves deliberately introducing faults into a system and
then measuring the results. This technique helps to identify and address
potential vulnerabilities and weaknesses in a system’s design, architecture, and
operational practices. Methods can include shutting down a service, injecting
latency (a time lag in the way a system responds to a command) and errors,
simulating cyberattacks, terminating processes or tasks, or simulating a change
in the environment in which the system is working and in the way it’s
configured. n recent experiments, we introduced faults into live cloud-based
systems to understand how they behave under stressful scenarios, such as attacks
or faults. By gradually increasing the intensity of these “fault injections”, we
determined the system’s maximum stress point. ... Chaos engineering is a great
tool for enhancing the performance of software systems. However, to achieve what
we describe as “antifragility” – systems that could get stronger rather than
weaker under stress and chaos – we need to integrate chaos testing with other
tools that transform systems to become stronger under attack.
Six pillars for AI success: how the C-suite can drive results
Many AI and GenAI solutions have common patterns and benefit from reusable assets that can accelerate time to value and reduce costs. Without a control tower, different groups across an enterprise are at risk of building very similar things from scratch for various use cases. The control tower effectively has authority over where an organization will make its investments and create value by identifying patterns across the various use cases that align with business needs and prioritizing the development of GenAI solutions, for example. ... The truly transformative impact would be to entirely reimagine what you do in the front office, not just streamline the back office. GenAI unlocks new products, services and business models that are easy to overlook if you approach the technology with a robotic process automation mindset. That can include creating new products and features enabled through GenAI, equipping them with connectivity under pay-as-you-go service subscription models, selling them directly to consumers instead of through intermediaries, and leveraging the consumer data for insights and perhaps selling it as a separate revenue stream.
Cyber Hygiene: The Constant Defense Against Evolving B2B Threats
By partnering with companies that provide early warnings about threats and scams
when they see them independently, such as domain spoofing attempts, businesses
can stay ahead of potential threats. “That’s an important control, and I
strongly recommend it for any company,” Kenneally said, stressing the benefits
of collaborative working partnerships. “It’s about ensuring that the controls
are in place and that we are partnering with our customers to mitigate risks,”
he added. This is particularly relevant given the increasing sophistication of
phishing attempts, some of which may be assisted by artificial intelligence.
Another aspect of Boost’s strategy is fostering a culture of resilience and
agility within the organization. This involves continuous training and
education, not just for the IT team but across the entire company. “Training is
critical,” Kenneally said. ... As the cybersecurity landscape continues to
evolve, the need for companies to protect their digital perimeter becomes more
pressing. But while the threats may change, the fundamental principles of good
cybersecurity — vigilance, education and proactive planning — remain
constant.
I’ve got the genAI blues
Why is this happening? I’m not an AI developer, but I pay close attention to the
field and see at least two major reasons they’re beginning to fail. The first is
the quality of the content used to create the major LLMs has never been that
good. Many include material from such “quality” websites as Twitter, Reddit, and
4Chan. As Google’s AI Overview showed earlier this year, the results can be
dreadful. As MIT Technology Review noted, it came up with such poor quality
answers as “users [shoud] add glue to pizza or eat at least one small rock a
day, and that former US president Andrew Johnson earned university degrees
between 1947 and 2012, despite dying in 1875.” Unless you glue rocks into your
pizza, those are silly, harmless examples, but if you need the right answer,
it’s another matter entirely. Take, for example, the lawyer whose legal
paperwork included information from AI-made-up cases. The judges were not
amused. If you want to sex chat with genAI tools, which appears to be one of the
most popular uses for ChatGPT, accuracy probably doesn’t matter that much to
you. Getting the right answers, though, is what matters to me and should matter
to anyone who wants to use AI for business.
AI technology brings significant benefits to the Financial Services sector,
including enhanced efficiency through automation, improved accuracy in risk
assessments, personalised customer experiences via AI-driven insights and
faster, more secure fraud detection. It also enables predictive analytics for
better decision-making in areas like investment and lending. ... AI is there
to support the employee – to elevate the human potential by delivering
insights, knowledge and expedite results. However, challenges include the
complexity of implementing AI systems, concerns around data privacy and
security, regulatory compliance, and potential biases in AI models that can
lead to unfair outcomes. Ensuring transparency and trust in AI decisions is
also crucial for its broader acceptance in the sector. ... Trustworthy AI also
ensures that compliance with regulations is maintained, risks are properly
managed and ethical standards are upheld. In a sector where customer
relationships are built on trust, any misstep could lead to reputational
damage, financial loss, or regulatory penalties.
Quote for the day:
“A dream doesn't become reality
through magic; it takes sweat, determination, and hard work.” --
Colin Powell
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