Dov Moran is a world-renowned inventor of a USB memory stick and the pioneer of several flash memory technologies. A managing partner of Grove Ventures, Dov is one of Israel’s most prominent high-tech leaders, visionary entrepreneurs, and investors. In an interview with the Ukrainian media, Dov Moran spoke about his brainchild, the future of startups, and how Ukraine can stay ahead of the curve in innovation. This is our English translation of the interview.
Although ML can take a while to be implemented, if the algorithms and network architectures are correctly aligned, the machine learning system will start producing the results that correspond to the actual ones.
ML as a rescue ranger for DevOps
Algorithms of machine analysis and learning allow you to monitor information objects (e.g., databases, applications, etc.)
The system determines itself how the objects should function adequately, and for additional adjustments, parameterization mechanisms would suffice.
Adjustment mechanisms help make the algorithms more accurate, as well as adapt them to specific needs.
Using ML can reveal anomalies in this data such as large amounts of code, long build times, extended release times and code checks, and identify many “deviations” in software development processes, including inefficient use of resources, frequent task switching, or slowing down the process.
0
On the other hand, you don’t want to sacrifice its quality by being too stringent.
Planning a software development budget is a tricky balancing act, that’s why it’s best to approach it with careful consideration.
Just how big your software development cost will turn out depends on a lot of factors.
Your software size, its overall complexity as well as the complexity of UI design, its integration with external systems and the necessity of data migration will determine the eventual budget.
The uniqueness of an application will also dictate its price: something niche and complex will usually require hiring skilled developers who cost more.
The price for powerful enterprise web apps may even exceed $1,000,000, although a lot will depend on a development company: larger companies will usually charge more.
0
Today, digital payment services and virtual banks compete with traditional banks in all areas, including payment, money transfer, and lending.
According to Jason Goldberg, an analyst at JPMorgan, the strongest competition between virtual and brick-and-mortar banks is in the payments category, where fintech startups pose a severe threat to traditional banking.
On the other hand, cooperation between traditional banks and fintech startups has increased significantly since the latter are much more dynamic and flexible in building and launching new technologies faster than cumbersome banks with complex hierarchy, lengthy decision making and overall resistance to change.
In October 2017, McKinsey published a study where it described the threats to the financial sector from fintech startups.
Founded in 2005 and headquartered in Stockholm, Klarna is estimated at $3.5 billion, which makes it the most expensive fintech startup in Europe.
The British money transfer service was launched in January 2011 by Cristo Kärmann and Taavet Hinricus.
0
In a recent statement, billionaire and Alibaba co-founder Jack Ma expressed his worries about tech regulation in Europe.
The region has not produced any large tech firms, unlike the United States and China.
Instead, Europe is leading the way when it comes to regulating large tech companies, particularly those who are developing AI-based solutions.
If the mentality is now a worry, you’ll worry all the time.” Jack Ma BeinCrypto reports that Ma openly questioned the EU’s political leanings when it came to define and articulate ethical guidelines for the development and usage of technology.
“Capitalist models focus on the ability for companies to innovate free of government regulation.
On the contrary, socialist models rely on government regulation and infrastructure to facilitate innovative developments,” Ma said.
0
Are you looking to pursue a career as a VR software developer or 3D designer/artist?
We’ve prepared a list of 8 online courses you can take advantage of to boost skills and gain firsthand experience with creating remarkable virtual reality and 3D visualization experiences for others.
This online course aims at helping anyone willing to learn Unity to create VR experiences targeting a device as simple as iOS/Android cardboard.
No previous programming experience is required.
This course is designed for intermediate to advanced Unity developers who want to build Virtual Reality applications for mobile platforms.
You’ll learn how to design, develop, troubleshoot, and publish your own mobile VR applications in Unity for Google Daydream, Gear VR, or Oculus Go devices.
0
IBM has recently estimated that customer service chatbots will help businesses all over the world save $8 billion per year by 2022.
The tables will turn from AI to IA in the majority of firms that have already dabbled in some form of AI, as they’ll quickly realize that their irrational exuberance for AI adoption must be equally met with solid efforts on an AI-worthy data environment.” Yet, talent shortage goes beyond data science and technical skills; today’s organizations need to focus on finding specialists capable of creative problem solving and resilience over coding and statistical skills.
“Firms are already combining AI building block technologies such as ML and text analytics with RPA features to drive greater value for digital workers in four use cases: analytics that solves nagging platform issues; chatbots that boss around RPA bots; IoT events that trigger digital workers; and text analytics that lifts RPA’s value.” What AI decision makers think about the business value of chatbots Attempting to evaluate what IT decision makers think about the business value of AI-driven chatbots, I reached out to them on Quora and got some thought-provoking answers that are worthwhile to be shared here.
0
This newly-emerged industry is now at the cutting edge of technology with 2,196 deals forged worldwide and global investment in fintech companies hitting $111,8 billion in 2018.
What previously involved going to a brick-and-mortar institution, waiting in lines, engaging in lengthy conversations with personnel, and reading the fine print, can now be done in just a few mouse clicks.
Powered by NLP and big data algorithms, this type of fintech software captures and analyzes client-related data to provide financial companies with a clear assessment of clients’ creditworthiness and possible risks.
Such systems generate documents, assigns tasks, helps track progress, and build client relationships.
It also manages billing processes, claims management, and pretty much an entire insurance agency life cycle.
For example, BIMA, an insurance service, uses mobile health-monitoring solutions to offer microinsurance to its clients in developing economies.
0
Machine learning (ML) and artificial intelligence (AI) have started to gain traction over the past years, and today, nearly every emerging startup is trying to leverage these technologies to attract funding and disrupt traditional markets.
And it’s true that companies using “AI” and “ML” as buzzwords in their pitch are more likely to attract external investments than their counterparts working with traditional and mainstream tech.But still, apart from all this hype around machine learning, how applicable is it for solving real-life, everyday problems and when does it make sense to use it instead of/together with traditional software programming?
Let’s start exploring the issue by describing the various types of machine learning and its basic principles.Machine Learning vs Traditional ProgrammingTo better understand how machine learning works, let’s look at how it differs from traditional programming.First of all, machine learning does not replace traditional programming, and a software developer will never use machine learning algorithms to create a website.
For example, ML can be used to build predictive algorithms for an online trading platform, while the platform’s UI, data visualization and other components will be implemented in a mainstream programming language such as Ruby, Python, or Java.The rule of thumb: only use machine learning when traditional programming methods are not effective/feasible for solving a particular problem.To better exemplify it, let’s consider a classical machine learning problem of exchange rate forecasting and see how it can be solved with the help of both techniques.In this article, we looked at three types of machine learning: supervised, unsupervised, and reinforcement.
Each of them has areas of practical application in real-world conditions and its own distinctive features.Supervised ML is by far the most developed and applicable form of machine learning to date.
Now there are dozens of ready-made classical algorithms for machine learning, as well as various Deep Learning algorithms for solving more complex problems, such as image, text, and voice processing.On the other hand, unsupervised machine learning is much less applicable in real life.
0
Dov Moran is a world-renowned inventor of a USB memory stick and the pioneer of several flash memory technologies. A managing partner of Grove Ventures, Dov is one of Israel’s most prominent high-tech leaders, visionary entrepreneurs, and investors. In an interview with the Ukrainian media, Dov Moran spoke about his brainchild, the future of startups, and how Ukraine can stay ahead of the curve in innovation. This is our English translation of the interview.
Are you looking to pursue a career as a VR software developer or 3D designer/artist?
We’ve prepared a list of 8 online courses you can take advantage of to boost skills and gain firsthand experience with creating remarkable virtual reality and 3D visualization experiences for others.
This online course aims at helping anyone willing to learn Unity to create VR experiences targeting a device as simple as iOS/Android cardboard.
No previous programming experience is required.
This course is designed for intermediate to advanced Unity developers who want to build Virtual Reality applications for mobile platforms.
You’ll learn how to design, develop, troubleshoot, and publish your own mobile VR applications in Unity for Google Daydream, Gear VR, or Oculus Go devices.
Although ML can take a while to be implemented, if the algorithms and network architectures are correctly aligned, the machine learning system will start producing the results that correspond to the actual ones.
ML as a rescue ranger for DevOps
Algorithms of machine analysis and learning allow you to monitor information objects (e.g., databases, applications, etc.)
The system determines itself how the objects should function adequately, and for additional adjustments, parameterization mechanisms would suffice.
Adjustment mechanisms help make the algorithms more accurate, as well as adapt them to specific needs.
Using ML can reveal anomalies in this data such as large amounts of code, long build times, extended release times and code checks, and identify many “deviations” in software development processes, including inefficient use of resources, frequent task switching, or slowing down the process.
IBM has recently estimated that customer service chatbots will help businesses all over the world save $8 billion per year by 2022.
The tables will turn from AI to IA in the majority of firms that have already dabbled in some form of AI, as they’ll quickly realize that their irrational exuberance for AI adoption must be equally met with solid efforts on an AI-worthy data environment.” Yet, talent shortage goes beyond data science and technical skills; today’s organizations need to focus on finding specialists capable of creative problem solving and resilience over coding and statistical skills.
“Firms are already combining AI building block technologies such as ML and text analytics with RPA features to drive greater value for digital workers in four use cases: analytics that solves nagging platform issues; chatbots that boss around RPA bots; IoT events that trigger digital workers; and text analytics that lifts RPA’s value.” What AI decision makers think about the business value of chatbots Attempting to evaluate what IT decision makers think about the business value of AI-driven chatbots, I reached out to them on Quora and got some thought-provoking answers that are worthwhile to be shared here.
On the other hand, you don’t want to sacrifice its quality by being too stringent.
Planning a software development budget is a tricky balancing act, that’s why it’s best to approach it with careful consideration.
Just how big your software development cost will turn out depends on a lot of factors.
Your software size, its overall complexity as well as the complexity of UI design, its integration with external systems and the necessity of data migration will determine the eventual budget.
The uniqueness of an application will also dictate its price: something niche and complex will usually require hiring skilled developers who cost more.
The price for powerful enterprise web apps may even exceed $1,000,000, although a lot will depend on a development company: larger companies will usually charge more.
Today, digital payment services and virtual banks compete with traditional banks in all areas, including payment, money transfer, and lending.
According to Jason Goldberg, an analyst at JPMorgan, the strongest competition between virtual and brick-and-mortar banks is in the payments category, where fintech startups pose a severe threat to traditional banking.
On the other hand, cooperation between traditional banks and fintech startups has increased significantly since the latter are much more dynamic and flexible in building and launching new technologies faster than cumbersome banks with complex hierarchy, lengthy decision making and overall resistance to change.
In October 2017, McKinsey published a study where it described the threats to the financial sector from fintech startups.
Founded in 2005 and headquartered in Stockholm, Klarna is estimated at $3.5 billion, which makes it the most expensive fintech startup in Europe.
The British money transfer service was launched in January 2011 by Cristo Kärmann and Taavet Hinricus.
This newly-emerged industry is now at the cutting edge of technology with 2,196 deals forged worldwide and global investment in fintech companies hitting $111,8 billion in 2018.
What previously involved going to a brick-and-mortar institution, waiting in lines, engaging in lengthy conversations with personnel, and reading the fine print, can now be done in just a few mouse clicks.
Powered by NLP and big data algorithms, this type of fintech software captures and analyzes client-related data to provide financial companies with a clear assessment of clients’ creditworthiness and possible risks.
Such systems generate documents, assigns tasks, helps track progress, and build client relationships.
It also manages billing processes, claims management, and pretty much an entire insurance agency life cycle.
For example, BIMA, an insurance service, uses mobile health-monitoring solutions to offer microinsurance to its clients in developing economies.
In a recent statement, billionaire and Alibaba co-founder Jack Ma expressed his worries about tech regulation in Europe.
The region has not produced any large tech firms, unlike the United States and China.
Instead, Europe is leading the way when it comes to regulating large tech companies, particularly those who are developing AI-based solutions.
If the mentality is now a worry, you’ll worry all the time.” Jack Ma BeinCrypto reports that Ma openly questioned the EU’s political leanings when it came to define and articulate ethical guidelines for the development and usage of technology.
“Capitalist models focus on the ability for companies to innovate free of government regulation.
On the contrary, socialist models rely on government regulation and infrastructure to facilitate innovative developments,” Ma said.
Machine learning (ML) and artificial intelligence (AI) have started to gain traction over the past years, and today, nearly every emerging startup is trying to leverage these technologies to attract funding and disrupt traditional markets.
And it’s true that companies using “AI” and “ML” as buzzwords in their pitch are more likely to attract external investments than their counterparts working with traditional and mainstream tech.But still, apart from all this hype around machine learning, how applicable is it for solving real-life, everyday problems and when does it make sense to use it instead of/together with traditional software programming?
Let’s start exploring the issue by describing the various types of machine learning and its basic principles.Machine Learning vs Traditional ProgrammingTo better understand how machine learning works, let’s look at how it differs from traditional programming.First of all, machine learning does not replace traditional programming, and a software developer will never use machine learning algorithms to create a website.
For example, ML can be used to build predictive algorithms for an online trading platform, while the platform’s UI, data visualization and other components will be implemented in a mainstream programming language such as Ruby, Python, or Java.The rule of thumb: only use machine learning when traditional programming methods are not effective/feasible for solving a particular problem.To better exemplify it, let’s consider a classical machine learning problem of exchange rate forecasting and see how it can be solved with the help of both techniques.In this article, we looked at three types of machine learning: supervised, unsupervised, and reinforcement.
Each of them has areas of practical application in real-world conditions and its own distinctive features.Supervised ML is by far the most developed and applicable form of machine learning to date.
Now there are dozens of ready-made classical algorithms for machine learning, as well as various Deep Learning algorithms for solving more complex problems, such as image, text, and voice processing.On the other hand, unsupervised machine learning is much less applicable in real life.