What the hell is an AI factory?
Here’s how the AI factory works. Quality data obtained from internal and
external sources train machine learning algorithms to make predictions on
specific tasks. In some cases, such as diagnosis and treatment of diseases,
these predictions can help human experts in their decisions. In others, such
as content recommendation, machine learning algorithms can automate tasks with
little or no human intervention. The algorithm– and data-driven model of the
AI factory allows organizations to test new hypotheses and make changes that
improve their system. This could be new features added to an existing product
or new products built on top of what the company already owns. These changes
in turn allow the company to obtain new data, improve AI algorithms, and again
find new ways to increase performance, create new services and product, grow,
and move across markets. “In its essence, the AI factory creates a virtuous
cycle between user engagement, data collection, algorithm design, prediction,
and improvement,” Iansiti and Lakhani write in Competing in the Age of AI. The
idea of building, measuring, learning, and improving is not new. It has been
discussed and practiced by entrepreneurs and startups for many years.
SaaS : The Dirty Secret No Tech Company Talks About
The dirty little secret I have found is that, in most cases, this promised
state just isn’t the case. The more SaaS companies I’ve seen, the more I’ve
witnessed great companies forced to become service businesses to scale. Having
a services team isn’t bad; it can even produce a lot of benefits for
customers. But many times it ends up being necessary in SaaS. As with all
things that involve consultants, it’s going to take longer and cost more to
get your product(s) live. Put frankly, this process sucks, and it’s not the
SaaS dream. Especially today, when organizations need to do more with less,
adding heads just to get your product live seems like another problem to deal
with, not a solution. SaaS products were supposed to be delivered via the
cloud almost instantly. The same SaaS product was going to work for every
customer, and once we built a brand, it was gonna be glorious. WTF happened?!
I grew just as frustrated as some of you likely are. As part of the founding
team at Behance, I lived this myself. We built a beautiful portfolio-sharing
platform employed by millions of people, which we eventually sold to Adobe.
Our platform became the engine that powered portfolios for design institutions
including the Rhode Island School of Design (RISD), Savannah College of Art
and Design (SCAD), School of Visual Arts (SVA), and the American Institute of
Graphic Arts (AIGA), among others.
Top 7 NLP Trends To Look Forward To In 2021
With advances in NLP and the increasing demands in customer service, one can
expect major strides towards next-gen bots that can hold complex
conversations, self-improve, and learn how to carry out tasks that have not
been previously trained on. Due to a rise in remote working situations in
2020, there has also been a tremendous increase in customer support tickets
across industries. It has become a major task to deal with increased ticket
volume and provide quick responses to urgent queries. One can expect the
integration of NLP tools with help desk softwares to perform tasks such as
tagging and routing of customer support requests, thereby requiring human
intervention in just higher-value tasks. The success of automated machine
learning or autoML in effectively dealing with real-world problems has
prompted researchers to develop more automation and no-code tools and
platforms. One such area is automation in natural language processing. With
AutoNLP, users can build models like sentiment analysis with just a few basic
lines of code. This encourages wider participation in the machine learning
community, earlier thought to be restricted to just developers and engineers.
AI Is Reengineering All Aspects Of Our Human Experience: What Are The Implications?
We have come together to fight Covid-19 and AI was a key enabler to bring to
market vaccines, in unprecedented clinical trial R&D timeframes, to
eradicate this virus, and help us get back to a more interactive global
community where we can freely travel, visit our favourite restaurants and shop
with more access in our local retailer stores. This is an excellent example of
AI being used for good. However, much of AI in large global data sets are full
of inequalities, incumbencies and biases of the innovators designing AI which
have a direct impact on how the technology guides human information,
perception and action. As AI leads society towards the next phase of human
evolution, it is becoming increasingly more evident that we need to acutely
increase our knowledge of AI ethics and reflect on the future world we
want to create, otherwise, we will be creating AI models that are
sub-optimal to align with our values. Can we create an intelligence that is
unconstrained by the limitations and prejudices of its creators to have AI
serve all of humanity, or will it become the latest and most powerful tool for
perpetuating and magnifying racism and inequality?
SMBs: How to find the right MSP for your cybersecurity needs
Outsourcing cybersecurity appears to be the wisest choice for most SMB owners.
"Small- to medium-sized businesses are aware of the importance of IT security,
but they don't always have the same resources or technical ability to deal
with them as larger enterprises do," says Adam Lloyd, president and CEO of
North American MSP Pioneer Technology, in the Channel Futures article. "As a
result, they expect their managed service provider (MSP) to act as a true
security partner to point them in the right direction and ensure the
technology they have in place will protect them and their data." Courchesne
explains what to look for when determining which is the best MSP for providing
cybersecurity services. The first step is to look at the service provider's
strengths and weaknesses. "If providers work only with cloud services ('born
in the cloud' MSPs) or look to speed deployment to new customers and easily
manage all clients through a single console, they will work best with
cybersecurity delivered as-a-service that can be overseen through a
cloud-hosted console," he writes. Then there are service providers that have
developed their own cybersecurity platform; this allows the provider to focus
on customers who have a more complex IT infrastructure.
How to Transform Your Cybersecurity Posture
Traditionally, cybersecurity has been seen as the department that says “no.”
Cyberfolks are known for insisting on extra testing, identifying last-minute
vulnerabilities, and causing cost overruns and delays. However, this
reputation isn’t altogether fair. Rather, it results from the fact that cyber
experts are excluded from the early stage of a project. On the other hand, if
you include these experts at the outset, design and development can be
accomplished in a way that’s both more secure and more profitable. According
to primary research from the Boston Consulting Group (BCG), whose
cybersecurity practice I lead, such early equity cuts the amount of rework by
up to 62%. Such savings reduce not only development time and cost, but also
time to market. What’s more, in gaining a seat at the table, cyber experts
become pathfinders who shine a light on the quickest, most cost-effective, and
securest routes. They’re no longer curmudgeons who say “no,” but collaborators
who are invested in getting to “yes” — and sooner rather than after afternoon
coffee break. The Cloud - For companies in the midst of a cloud journey, the
benefits of security by design are dramatic. Because so much of the
infrastructure in cloud-based systems is created with software code, that
“infrastructure as code” can be reused by hundreds of apps and checked
continuously by automated “audit-robots.”
7 Trends Influencing DevOps/DevSecOps Adoption
From massive, inflexible systems that limit compatibility, the new trend of
concise, compatible software has increased the adoption of DevOps and
DevSecOps substantially. With architectures such as containers becoming
mainstream, it has become easier than ever for teams to code, debug, and
deploy faster. The computerized, un-editable logging has made work
transparent. The lightweight choice has made projects free for development on
any platform, kept in sync via the internet. Endorsing microservice
architecture gives the benefit of install, run, maintain systems.
... Stemming off the microservices trend, mobile-first cloud-first
development has worked wonders for data transportation, security, and
collaboration. On all grounds—efficiency, safety, transparency,
collaboration—the cloud-first adoption has made development perpetual,
seamless, and efficient. In many ways, adapting to the trendy, cloud-first
architecture is directly integrating a part of the DevOps work cycle into the
company, making it promotive of DevOps/DevSecOps adoption in technology
organizations. ... In IT, infrastructure is the foundation that deals
with software, hardware, networking resources, systems, and tools that allow
companies to operate and manage their production processes.
The evolution of digital banking post COVID-19
As more and more people and businesses rely on digital apps for their banking
services, the number of online transactions continue to grow; putting a strain
on existing IT computing resources. The massive increase in the number of
queries is resulting in bottlenecks that can degrade the performance of
applications and affect customer service levels. When customers wait too long to
complete a transaction or receive approval for a loan, or if they understand
that they can receive better conditions from another bank, they are more likely
to switch. Thus, banks are faced with the need to scale up their expensive
legacy infrastructure to provide the expected user quality of experience, or to
find modern solutions that can elastically scale to manage this data at the
required speeds, with an optimized TCO. In many cases large financial services
organizations are limited by tangled and archaic systems that are too complex to
optimally manage, process and analyze their huge amounts of data from different
sources. This was revealed recently in a BIAN survey where over 60 percent of
respondents expressed concerns that banks will struggle to open up their APIs
because of the “current state of banks’ core architecture.”
Don’t Do Agile and DevOps by the Book
That’s the short version and there’s a huge range of books and frameworks out
there to read so that anyone, anywhere–apparently–can just start doing it. The
danger is that if you follow them too closely, processes can actually become
too rigid, so you end up losing the agility you’re striving for. I always get
suspicious when theories in books are read and regurgitated completely without
thinking about the actual situation on the ground. I’d much rather have a
conversation, write up our notes, try it out and see how it can be improved.
Clearly, I’m not saying that you shouldn’t have boundaries and rules. I worked
for a company that moved from no processes at all to adopting Agile
methodologies. It needed to put in place a framework to guide people in the
right direction, particularly initially. As companies mature though, they need
to look at what works best for their particular situation–otherwise the danger
is that common practice masks commonsense. You end up following processes,
such as two-weekly reviews, that don’t necessarily match your needs–why wait
two weeks for a review, for example, if something obviously needs fixing
today? Where did Agile go? The best place to start is to define Agile for your
organization.
Europe has a unique opportunity to lead in the democratisation of artificial intelligence
The issue as such is less whether AI will be diffused and democratised, but
what the different scenarios for its potential diffusion will be; whether
democratisation can work in favour of collective value creation or to entrench
existing market power; whether there will be empowering, enabling, and
inclusive standards or extractive institutions and practices; whether
democratisation can empower a new generation of firms and citizens or whether
it will establish the second digital divide. This question compounds.
Responsible democratisation means that human centric and user centric
standards need to be broader, to consider what happens when a multitude of
such standards interact with one another, when AI applications interact and
compete inter-culturally and internationally. Indeed, there are no
value-neutral AI applications. We cannot expect the divisions to be clear;
rather they will be murky, mixed between exceptionally novel solutions for
public value and highly extractive institutional frameworks, with both
corporate and government uses of such technologies. The focus should be to
look beyond ethics, towards the political economy, which determines which
ethical approaches will succeed or not.
Quote for the day:
“Knowledge has to be improved, challenged, and increased constantly, or it vanishes” -- Peter F. Drucker
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