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.
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.
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.
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?
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.
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.”
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.
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.”
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.
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