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ZDNet takes a look at the AI and Machine Learning predictions, in what the business environment is concerned. Expected to surge in 2019, the rapid adoption of these new technologies also brings different business challenges.

The article mentions the necessary (and huge) shift that is absolutely required for organizations to thrive. Going from buying “black boxes that will fix our problems and offer us solutions” – as the publication puts it, to the next stage, won’t be easy.

A passive attitude welcomes possible cases of “algorithm sprawl, project management issues, and vendor hype”.

But what modern, wise organizations do? How can we all avoid the so-called bad implementations of AI & ML?

 

 

Who will have to handle AI data?

Since data science is not a magic  trick – although sometimes it may seem so – in order for it to work, key people in your company come in. Apparently, “everyone from mid-level managers to C-level execs are going to use this AI”. They also need to understand how to use it, to be prepared and aware of this new age of technology.

Therefore, determining who will work with your AI data is absolutely necessary. You need the big picture before going into details. However, getting the right big picture requires experience.

Use experts – get consultancy as an ally in this big battle. You want to understand what will be going on, and someone who can put the process into perspective can make you go a long way. Having worked with systems, apps, connected data, Artificial Intelligence and data science, professional consultants can provide a reliable blueprint. How will it work for my business? The answer to this is much more accurate when you obtain it with the right guidance and assistance.

 

What does your AI data need to provide answers for?

As the article we’ve mentioned lists, operating Big Data generates effects that go beyond the lucrative insights.

You will also need to have a strategy and to deliver reports to your board.

As the process evolves at a global level, you will need to choose the most suited AI services pack – from the big tech cloud providers.

Data scientists are still scarce, and the models you will be dealing with lack transparency.

The AI-trained professionals are also scarce, and this specialization just made its way into the academic curriculum. For now, hands-on experience prevails.

Your AI data needs to provide answers and guidelines for all of the above, and more. You will be working with a tool with huge potential that you barely understand. Nevertheless, you need to move relatively fast while making the best choices. The AI tools unleash their potential on the go. Add an extra layer of software solutions – and you have a new functionality. Get on board with the best cloud provider, and you will be able to deploy your tools next-level style.

 

Prepare for the next step, by getting a partner that understands the new technology

It’s not only AI. As we’ve seen, it’s also Machine Learning, Big Data, algorithms, analytics, IoT, embedded sensor software, various layers that have to integrate with each other.

Working all these out into the strategy of your company, while resetting certain functionalities and expanding others – this won’t be easy.

As all things good and new, as overwhelming as it may seem, it needs to be done. The sooner, the better. Make the first step and choose a reliable software partner, one experienced in business digital transformation. Call, talk, schedule the meetings you need, soon you will have a whole new perspective on the matter.

 

We are here for you – our know-how and experience are both rich, and our eagerness and innovative spirit are the right match for this future of technology.

Contact us and let’s find out more about how a potential partnership may look like, and what it can do for both of us!

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Tech

Wearables rank on top of the currently available (and emerging) digital solutions for preventive healthcare. Studies anticipate that the mass-scale implementation of wearables would change human behavior. All this in ways whose amplitude should match the other big digitally-induced changes that we’ve experienced in the past decade.


Computers would have morphed into a new, progressive sense of the term. One of the sources we’ve browsed introduces the “collective wearable” notion. By carrying wearables around, people will provide mobility. Mobility means an enhanced digital perception for this huge network of sensors.


Of course, these devices need to collect, deliver, and answer to commands based on processed data, a thing that requires software. A new generation of software for miniaturized devices would roam around, in the shape of watches, glasses or even part of our garments.


Preventive healthcare, giving wearables a noble use



Which one of us can say he or she doesn’t care to find out details about their health? When it comes to self-centered preoccupations, the adults of today are more responsive than those of past days. We may even compete with kids in our eagerness to connect our wellbeing with the digital environment. Who could resist a visually attractive, efficient and well-promoted device? Especially when it provides precious insights about our body. We would have metrics about the way we sleep, breathe, and exercise, about various physical systems and functions.


Of course, these devices would also link the people who wear them with their physicians. Remember a couple of years ago a nanotechnology that reported whether a patient took the prescribed pills or not? Similarly, doctors could monitor the persons in their care . Remotely. The software layer of this wonderfully connected system would deliver a continuous or periodic stream of data. The wearer would link straight into the dedicated software, into the medical network itself. Notifications and/or warnings could prompt the care professionals to intervene when needed.


The actual healthcare potential is huge. So is the preventive healthcare digital market. With a couple of problems out of the way, the manufacturers can expect a boom. For once, the measurements need to be accurate. Another major issue already publicized would be the cyber-security one. No one wants to carry around his/her wrist an open invitation to hackers everywhere.


Both risks point out a key element – quality is needed. High-quality embedded software and software engineering in all layers, from the UI to the data processing system.


Perspectives


How to be successful and build/maintain a notable reputation in the healthcare wearables market? All the involved entities need to make sure quality and reliability are their main goals. With the potential audience spanning worldwide, you would want to get it right from the first time.

Software solutions partners that have accumulated experience in digital healthcare so far are a wise way to go.

As you may have noticed in other posts, we have the know-how and experience that your digital healthcare project needs. Contact us for details!

 

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News

ZDNet explores the way AI, the cloud, and Big Data in “the AI regeneration era”. The industry dubbed the adjacent infrastructure stack Industry 3.0. Due to the new generation of AI chips, data-centric software tasks should benefit in terms of both operational databases and analytics, as well as in what machine learning (ML) implies.

The easier way of handling Big Data can make companies of all sizes and from any location access new operational horizons. But organizing Big Data processing still involves a series of key decisions, which often call for experienced consultants. The pharmaceutical companies have a number of hard choices in the cloud and on-premise already laid out for them.

Bear in mind that the LASTING Software implementation of analytical algorithms and engines for statistical analysis is at the base of the world leading, FDA-approved solution. Our solution is being used by 93% of the world’s pharmaceutical companies. We will therefore walk you through the main expected pharma industry challenges, inspired by the article we mentioned.

 

The importance of processing units in accelerating your software workloads

It’s more exactly about GPUs – Graphical Processing Units, the ones that “leverage parallelism” and better keep up with Moore’s law. Their architecture responded to the new challenges well, and now one of the GPU main producers (NVIDIA), announced a set of innovative products with a new architecture.

Hardware to match the upgraded modern request is therefore on the way. But the software is also of importance. Seeing how “how GPUs are currently the AI chip of choice for ML workloads”, the ML libraries come into play.

For detailed recommendations, you may access the original article. What you need to remember is that “GPUs can greatly accelerate workloads that can be broken down in parts to be executed in parallel”. Enough said.

 

Field Programmable Gateway Arrays and their software scope

FPGAs, simplistically describable as “boards containing low-level chip fundamentals, such as AND and OR gates” are not new. Specific tasks or applications find their correspondence in the hardware description language (HDL) that specifies the FPGAs’ configuration.

Changing the said configuration at need suffers from a certain software layer immaturity. This time, the player that stands out is Intel. By investing into FPGAs R&D, this company tries to catch up on GPUs with a new line of next-gen FPGAs.

Again, the software is crucial. Along with it, the databases and libraries need to support the FPGA-accelerated analytics.  

 

Once having decided what you want, different choices ensue

To quote our inspirational article of the week: “Should you build your own infrastructure, or use the cloud? Should you wait until offerings become more mature, or jump onboard now and reap the early adopter benefits? Should you go for GPUs, or FPGAs? And then, which GPU or FPGA vendor?”

You may check some of the details and possible answers put forth by the author.

If you can make sense of them, or even get the big picture, then you must be familiar with both hardware and software – kudos to you.

Even so, the activity of your company might need the time to focus on different matters. You can still use a partnership where you can state what you need, infrastructure-wise, and your software solutions partner would deliver it.

Unable to follow the detailed options presented by ZDNet? Then the assumption that your company is a typical pharmaceutic industry entity could be the right one. No need to get stuck trying to learn software-specific notions or trying to make sense by yourself what would the best hardware elements be.

 

The multitude of available options pushes for the right partnerships. The wisest, poised for efficiency and success organizations learn to delegate tasks. To make next-gen digitization simple and get right to the point where you benefit from it, find the right software solutions partner.

We are waiting for your email or call!

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Tech

Industry 4.0 is about to dawn upon all industries. It means cyber physical systems, IoT, networks and everything between. It means software solutions and services taking over every possible functionality. You will see your business transforming into its upgraded, better, faster, mobile version.

Also referred to as a sum of trends, this phenomenon companies have already started to experience is not an “if” anymore, but a “when”. To find out what exactly can you expect from it, we recommend you an article this week. It is authored by Pete Durand, an industrial automation expert with a vast experience in the field. This article sets out to reveal and exemplify what it means “to modernize manufacturing processes, manage risk, and improve the accuracy, distribution and integrity of data”.


The main pillars of the Industry 4.0 stage

 

Most of you would have heard of these key elements/area by now. The surrounding publicity is massive. The most inquisitive entrepreneurs were surely aware of them, even before the online media dedicated huge amounts of materials to these main topics.


The author chooses to focus on:

 

– IoT (with a twist, since he introduces the IIoT);

– Big Data, as a “a natural progression beyond IIoT”;

– Cybersecurity, as a critical need of data protection in a hyper connected world.

 

From his professional experience, Mr. Durand extracts yet another underlining element of importance. Documentation is a must have. Keeping a company on the forefront of technology makes documentation increasingly critical. The personnel may change. Thus, any organization needs to have a continuously updated history of what, when and where was set up, configured, build, implemented and maintained. When outsourcing, make sure to document changes, in partnership with your service providers.


Think about it…


Yet another thing that all companies need to be aware of – industry 4.0 is not cost prohibitive anymore. A multitude of devices are now available, and they all do their job. With due diligence and the proper consultancy, the best match for your business is not difficult to find. Capturing the right data is the next affordable step, then you need to upload it into the cloud type of choice (public, private or hybrid). Well, actually it is more a question of direct transmission. Process the data, get the key insights, deliver these to the decisional factors, and then repeat. In a nutshell, Industry 4.0 delivers the power of ubiquity and globalization, as well as the means to harness it, to your organization.


Partnerships are the future in the Industry 4.o


The scope of a company’s activity might be different from anything that has to do with software or even IT in general. Keeping that focus implies partnerships with software solutions companies. Consultants, software developers and software managers deliver the solutions you need.

Technology becomes viable, efficient and easy to work with. Translating needs and requirements into digital tools, your software solution partners allow your entire business to upgrade to the Industry 4.0 stage. Together with them, you can even provide your own customers with tailor-made software solutions.

What do you think of this perspectives?

 

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Tech

Code reviews are part of any software development environment. Not the most pleasant part, in what a developer is concerned, since it involves his/her work being scrutinized and it may feel stressful, “but it’s also one of the best ways to get feedback on your code, to catch typos and mistakes, and, more broadly, to grow as an engineer”.

In order to take some unnecessary pressure out of this process, there are tips & tricks synthesized in the online media that help both code developers and code reviewers make the best out of this stage. We asked a few collegues here at LASTING Software to help us select a few best practices/rules/recommendations.

Let’s review some of them, starting with the most quantifiable ones.

 

5 practical rules for code review

 

A developer should review less than 400 lines of code at once;

Keep the inspection rate at a density of less than 500 LOC per hour;

Try not to surpass 60 minutes at once without taking a break, if you want to keep a high performance;

Establish tangible, measurable goals for the entire process;

Use code review procedures that can be followed and tracked (checklists, collaborative tools, fixing processes to be implemented).

(Selected from Best Practices for Code Review, as presented by SmartBear)

 

5 recommendations for developers

 

Prepare a quick summary where you explain the problem and the proposed resolution;

Auto-review, prior to someone else reviewing your code, so you can trim some of the problems yourself;

Communicate – by using annotations to highlight all the soft spots, and tag the right people involved in the project that might clarify these problems you feel you need a second opinion about;

When the reviews are back, remember the importance of team work and concern yourself with their utility and processing prior (and over) feeling anxious;

Refactor changes without altering behavior.

 

5 recommendations for code reviewers

Always keep in mind the code purpose and the project scope;

Ask clear questions whenever the functions and classes reason does not reveal itself;

Be clear when highlighting the problem you have identified, and propose alternative solutions;

Write the reviews in a neutral and concise manner and avoid absolute judgments;

Don’t forget to mention the good things you notice – keeping it clear you review with the overall quality in mind, not just to hunt the errors.


You may find more useful advice, examples, and technical guidelines related to code reviewing in our third source of inspiration for this article – on Medium.

 

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