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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

Deep learning aims to make computers mimic the human brain. It is “inspired by and based on the model of the human brain to create artificial neural networks for machines”. The aim is to make machines express themselves and act in a similar way to us.

There is no degree of passion about this topic that equals the one met with the professionals that develop intelligent machines. Therefore, we want to recommend you a specific article this week.

 

Working in deep learning goes beyond tasks, into enthusiasm

The article pointed out by one of our team members is co-authored by Ronald van Loon and Rodrigo Agundez. The latter is “very enthusiastic about the improvements that deep learning can offer”.

The focus here is on the importance of the technological progress we are witnessing in real time. The post presents as well the huge work involved in the back-end algorithms. As the author says:


Creating machines that are capable of understanding minute differences in words embedded in a context may seem like a very small thing, but requires a very large set of data and complex algorithms to execute.


The accuracy is extremely important in differentiating complex and successful algorithms from the rest. You will see how and why deep learning is superior to AI (Artificial Intelligence), by going through the original post.


Our take


Both the authors benefit from hands-on experience in the field, and share the same passion for advanced technology. This is specific to our team members, too. We can easily relate to their enthusiasm by replaying our own experiences.

 

The article we recommend also mentions how the ubiquitous connectivity that now links software engineers around the globe makes it easier for the cutting edge companies to hire talented developers. Having access to research and open source data, as well as enriching one’s experience continuously through each new project, really opens up the horizon.

 

Once again we feel elated about our field of work – and this is the basic premise for great things!

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Tech

by Ecaterina Ganenco, Business Intelligence Developer, LASTING Software
(Presentation held during Codecamp Timisoara, April 2018)

The story of how developing a consumer mindset testing software influenced my way of thinking


Have you ever wondered why movies in Cineplex’s are scheduled? Why are they not always starting at the same time, or how managers show more comedies in a country, whereas a continent prefers romance better?

Before being part of the development team I used to go the movies rarely and mostly to Russian movies, which were more popular in my area.

The Movie Scheduling Problem and its data resolution

When people come to see a movie, they are following through a previous decision.

Decision-making is the process of identifying and choosing alternatives. It is based on the values and preferences of the decision maker. It also is a cognitive process.

Reverse engineering this cognitive process resulted in a smart software. A software capable of anticipating the selection of movie shows that would gather the most spectators. Among several alternative possibilities, the software aimed at pointing out the most suited scheduling formula.

The factors computed into such software are:

  • Decisional balance
  • Simple prioritization
  • Consulting with a person in authority
  • Anti-authoritarianism traits
  • Automated decision support (rule based)
  • Decision support systems (decision-making software)

The difficulty consisted in overcoming the decision-making paradox. Sometimes people decide predictably, while other times their choices may seem random. Yet, there are a few steps that would sift out the random factors as much as possible:

Formal analysis ->Covered problems -> Generalized set partitioning problem

Remember how historical data works magic? Inputting the historical data related to moviegoers serves in establishing patterns. We may not be able to eliminate the random element in decision making. But we can find the patterns in the outcome of the decisions, taken as historical data.

The app aimed to plan the best strategy. Finding out the optimal movie program for a single day given meant that it required a lot of details. The capacities of the screening rooms, the technical capabilities of this rooms, the list of movies to be shown or the set of theatre specific constraints are all important.

The formula is not unusual in big data apps. The users generate data – data is collected and packaged – the software processes the data – the software returns the data. Based on the returned results (predictive analysis), those who use the software can now make their own efficient decisions.

While working in software development, my focus is on the technology. However, my personal experience is that my mindset can also change due to the value and impact of the solutions I’m working on.

When I worked on a tool serving in understanding the consumer mindset, I had the opportunity of figuring out its impact on more than one levels. Such tools use big data. Finding out how my work, how the efforts of my team turn into structured data and facts, was important for me.

Products and services need to reach the people who enjoy them. In other words, the consumers need the right offer at the right time. Determining these key parameters may be a software matter.

 

How it is started:

 

How do you help consumers to consume more?

It is very simple – you provide quality and the right services/products.  Companies achieve product uniqueness through an elaborate process. But in this process they must answer two main questions:

–  What do consumers want?

–  What do (their) customers want?

Answers vary, of course. Factors such as culture may trigger different answers. I asked myself “So what determines if culture matters?” There are subjective areas of culture, for example religion, education or history. These stand the chance of being extremely personal. Their effect is seen in choice variations between different background people. Other factors are unifying. Patterns or globalization will ensure similar choices for example

 

We are consumers of our (own) applications

Any developer may answer the questions above when imagining it is she/he who uses that precise software solution. I thought about what I like and dislike as an app user.

What I like

– The application should solve a problem
– Easiness to navigate between pages
– Design

What I dislike

– If the application doesn’t resolve the problem
– If the application is not user friendly
– Performance issues

If the result of my work falls into the first category, I feel motivated.

Don’t ignore the word of mouth

Word of mouth or viva voce is organic marketing in itself. It can boost a product. It can also point out what goes wrong with it, before it’s too late. So just think:

– What sort of conversations are people having about the product?
– When people share information about our program, does it tend to be positive or negative, emotionally charged or indifferent?

Defining consumer groups

My story is about putting my work into perspective. I made the exercise of thinking as an app user, to objectively review the outcome of my activity. Then I became fascinated by the functionality of the software product I worked on.

To gather the right data, a big data software program needs guiding. Putting data sources into groups serves as guidance, for example. Half of the work is done if you can define the consumers groups.

Since the app I worked on targeted people, we had to define consumer groups. In the case of movie scheduling, I noticed this was achieved in 2 ways. I then thought of how this applies to a software product, to an app. One needs to test & define consumers as a prerequisite to grouping.

Test

–  A/B Testing
– Set the time of research
– Ask the question which you want answered
– Try to find and granulate all the things you need

Define

– Apply statistic tests to data
– Identify the vulnerable people
– People that don’t like the application – Who are they? Why don’t they like it?
– People that like the application – What are the main reasons?

What we should also take into account when we apply the test to a specific group is hidden features.
– Look at the details
– Run emotional ideas
– Test the nonverbal language

Also, historical data can do wonders.


My story

As a member of the public, I felt that any movie theatre chain that would use such tools to accommodate me better won my vote of confidence.

I started to go to the movies more. Especially English-speaking movies. I changed my decisions, in appreciation of the software-supported work that movie theatres put in, to meet the consumers.

The consumers are just clients, in higher numbers. And clients are always the masters. Accepting to be served gracefully is an art. In the current times we are often served by algorithms and software programs. Benefitting from this modern services is also a wise step, as well as an art.

So, the next time you go see a movie that is perfect for a rainy evening in the city, send out a good thought for the people behind the algorithms. We salute you through the software that just translated your preferences into reality.

 

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