Quantum utility: The next milestone on the road to quantum advantage
“Quantum utility is a term that has only been coined recently, in the last 12
months or so. On the timeline that I’ve just described, there is a milestone
that sits between where we are now and the beginning of this quantum advantage
era. And that is this quantum utility concept. It’s basically where quantum
computers are able to demonstrate, or in this case, in recent demonstrations,
simulate a problem beyond the capabilities of just brute force classical
computation using sufficiently large quantum computational devices. So, in
this case, devices with more than 100 qubits,” she says. ... “It’s really an
indication of how close we are to demonstrating quantum advantage, and where
we can hopefully begin to see quantum computing computers serving as a
scientific tool to explore a new scale of problems beyond brute force,
classical simulation. So, it’s an indication of how close we are to quantum
advantage and ideally, we’ll be hoping to see some demonstration of that in
the next few years. No one really knows exactly when, but the idea is that
those who are able to harness this era of quantum utility will also be among
the first to achieve real quantum advantage as well.”
5 tips for switching to skills-based hiring
Skills come in a variety forms, such as hard skills, which comprise the
technical skills necessary to complete tasks; soft skills, which center around
a person’s interpersonal skills; and cognitive skills, which include problem
solving, decision making, and logical reasoning, among other skills. Before
embarking on a skills-based hiring strategy, it’s vital to have clear insight
into the skills your organization already has internally, in addition to all
the skills needed to complete projects and reach business goals. As you
identify and categorize skills, it’s important to review job descriptions as
well to ensure they’re up-to-date and don’t include any unnecessary skills or
vague requirements. It’s crucial as well to evaluate how your job descriptions
are written to ensure you’re drawing in the right talent for open roles.
Wording job descriptions can be especially tricky when it comes to soft
skills. For example, if your organization values someone who’s humble or
savvy, you’ll need to identify how that translates to a skill you can list on
a job description and, eventually, verify, says Hannah Johnson, senior VP for
strategy and market development at IT trade association CompTIA.
Could California's AI Bill Be a Blueprint for Future AI Regulation?
“If approved, legislation in an influential state like California could help
to establish industry best practices and norms for the safe and responsible
use of AI,” Ashley Casovan, managing director, AI Governance Center at
non-profit International Association of Privacy Professionals (IAPP), says in
an email interview. California is hardly the only place with AI regulation on
its radar. The EU AI Act passed earlier this year. The federal government in
the US released an AI Bill of Rights, though this serves as guidance rather
than regulation. Colorado and Utah enacted laws applying to the use of AI
systems. “I expect that there will be more domain-specific or
technology-specific legislation for AI emerging from all of the states in the
coming year,” says Casovan. As quickly as it seems new AI legislation, and the
accompanying debates, pops up, AI moves faster. “The biggest challenge here…is
that the law has to be broad enough because if it's too specific maybe by the
time it passes, it is already not relevant,” says Ruzzi. Another big part of
the AI regulation challenge is agreeing on what safety in AI even means. “What
safety means is…very multifaceted and ill-defined right now,” says Vartak.
Why and How to Secure GenAI Investments From Day Zero
Because GenAI remains a relatively novel concept that many companies are
officially using only in limited contexts, it can be tempting for business
decision-makers to ignore or downplay the security stakes of GenAI for the
time being. They assume there will be time to figure how to secure large
language models (LLMs) and mitigate data privacy risks later, once they’ve
established basic GenAI use cases and strategies. Unfortunately, this attitude
toward GenAI is a huge mistake, to put it mildly. It’s like learning to pilot
a ship without thinking about what you’ll do if the ship sinks, or taking up a
high-intensity sport without figuring out how to protect yourself from injury
until you’ve already broken a bone. A healthier approach to GenAI is one in
which organizations build security protections from the start. Here’s why,
along with tips on how to integrate security into your organization’s GenAI
strategy from day zero. ... GenAI security and data privacy challenges exist
regardless of the extent to which an organization has adopted GenAI or which
types of use cases it’s targeting. It’s not as if they only matter for
companies making heavy use of AI or using AI in domains where special
security, privacy or compliance risks apply.
US, UK and EU sign on to the Council of Europe’s high-level AI safety treaty
The high-level treaty sets out to focus on how AI intersects with three main
areas: human rights, which includes protecting against data misuse and
discrimination, and ensuring privacy; protecting democracy; and protecting the
“rule of law.” Essentially the third of these commits signing countries to
setting up regulators to protect against “AI risks.” The more specific
aim of the treaty is as lofty as the areas it hopes to address. “The treaty
provides a legal framework covering the entire lifecycle of AI systems,” the
COE notes. “It promotes AI progress and innovation, while managing the risks
it may pose to human rights, democracy and the rule of law. To stand the test
of time, it is technology-neutral.” ... The idea seems to be that if AI does
represent a mammoth change to how the world operates, if not watched
carefully, not all of those changes may turn out to be for the best, so it’s
important to be proactive. However there is also clearly nervousness among
regulators about overstepping the mark and being accused of crimping
innovation by acting too early or applying too broad a brush. AI companies
have also jumped in early to proclaim that they, too, are just as interested
in what’s come to be described as AI Safety.
Fight Against Ransomware and Data Threats
Ransomware as a Service (RaaS) is becoming a massive industry. The tools to
create ransomware attacks are readily available online, and it’s becoming
easier for people even those with limited technical skills to launch attacks.
We have the largest pool of software developers in the world, and
unfortunately, a small portion of them see ransomware as a way to make easy
money. There are even reports of recruitment drives in certain states to hire
engineers or tech-savvy individuals to develop ransomware software. ... The
industries most affected by ransomware tend to be those that are heavily
regulated, such as BFSI (Banking, Financial Services, and Insurance),
healthcare, and insurance. These industries deal with highly valuable,
critical data, which makes them prime targets for attackers. Because of the
sensitive nature of the data they handle, these organizations are often
willing to pay the ransom to get it back. The reason these industries are so
heavily regulated is that they’re dealing with data that is more critical than
in other industries. Healthcare companies, for example, are regulated by
agencies like the FDA in the U.S. and their Indian equivalent. Financial
services are regulated by the RBI or SEBI in India.
Cloud Security Assurance: Is Automation Changing the Game?
For cloud workloads, security assurance teams must assess and gather evidence
for each component’s adherence to security standards, including for components
and configurations the cloud provider runs. Luckily, cloud providers offer
downloadable assurance and compliance certificates. These certificates and
reports are essential for the cloud providers’ business. Larger customers,
especially, work only with vendors that adhere to the standards relevant to
these customers. The exact standards vary by the customers’ jurisdiction and
industry. Figure 3 illustrates the extensive range of global,
country-specific, and industry-specific standards Azure (for example) provides
for download to their customers and prospects. ... These cloud security
assurance reports cover the infrastructure layer and the security of the cloud
provider’s IaaS, PaaS, and SaaS services. They do not cover customer-specific
configurations, patching, or operations, including securing AWS S3 buckets
against unauthorized access or patching VMs (Figure 4). Whether customers
configure these services securely and put them adequately together is in the
customers’ hands – and the customer security assurance team must validate
that.
The Road from Chatbots and Co-Pilots to LAMs and AI Agents
We are beginning an evolution from knowledge-based, gen-AI-powered tools–say,
chatbots that answer questions and generate content–to gen AI–enabled ‘agents’
that use foundation models to execute complex, multistep workflows across a
digital world,” analysts with the consulting giant write. “In short, the
technology is moving from thought to action.” AI agents, McKinsey says, will
be able to automate “complex and open-ended use cases” thanks to three
characteristics they possess, including: the capability to manage
multiplicity; the capability to be directed by natural language; and the
capability to work with existing software tools and platforms. ... “Although
agent technology is quite nascent, increasing investments in these tools could
result in agentic systems achieving notable milestones and being deployed at
scale over the next few years,” the company writes. PC acknowledges that there
are some challenges to building automated applications with the LAM
architecture at this point. LLMs are probabilistic and sometimes can go off
the rails, so it’s important to keep them on track by combining them with
classical programming using deterministic techniques.
Are you ready for data hyperaggregation?
Data hyperaggregation is not simply a technological advancement. It’s a
strategic initiative that aligns with the broader trend of digital
transformation. Its ability to provide a unified view of disparate data
sources empowers organizations to harness their data effectively, driving
innovation and creating competitive advantages in the digital landscape. As
the field continues to evolve, the fusion of data hyperaggregation with
cutting-edge technologies will undoubtedly shape the future of cloud computing
and enterprise data strategies. The problems and solutions related to
enterprise data aggregation are familiar. Indeed, I wrote books about it in
the 1990s. In 2024, we still can’t get it right. The problems have actually
gotten much worse with the addition of cloud providers and the unwillingness
to break down data silos within enterprises. Things didn’t get simpler, they
got more complex. Now, AI needs access to most data sources that enterprises
maintain. Because universal access methodologies still don’t exist, we
invented a new buzzword, “data hyperaggregation.” If this iteration of data
gathering catches on, we get to solve the disparate data problem for more
reasons than just AI. I hold out hope. Am I naive? We’ll see.
Unlock Business Value Through Effective DevOps Infrastructure Management
Whatever mix of architectures an organization uses, however, the best strategy
is rooted in their specific needs, focusing on profitability and customer
satisfaction. Overly complex systems not only cost more, but they also reduce
the return on investment (ROI) and efficiency. Innovation delivers services to
customers faster and more efficiently than before. With the plethora of
technologies available today, it's imperative for organizations to be clear
about what provides real value to reduce the cost and time spent on
infrastructure issues. ... Adopting DevOps infrastructure management practices
encourages the use of solutions like IaC, making deployments more repeatable,
scalable, and reliable. Automation and continuous monitoring free up resources
to focus on a broader range of tasks, including security, developer
experience, and time to market. Robust documentation processes are critical to
preserve this culture of continuous improvement, efficiency, and productivity
over time. Should a project be handed to a new team, documentation helps
maintain continuity and can reveal historical inefficiencies or
issues.
Quote for the day:
“People are not lazy. They simply have
important goals – that is, goals that do not inspire them.” -- Tony Robbins
