Top Reasons to Migrate to Unity All Flash

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EMC Unity is modernizing the datacenter, delivering advances that simplify the task of keeping up with growing storage demands.  These are just some of the top reasons to migrate today.

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How to lead a successful data-literacy program: 6 steps

Article | February 25, 2020

Data insights are a critical element to success and businesses strive to use what they've learned Despite it still being a struggle—90% of data and analytics decision makers see increasing the use of data insights in business decision making as a priority, according to a Forrester report, "Data Literacy Matters: The Writing's On The Wall." "Organizations need to invest in data literacy, and by that we don't mean just improving the skills of business analysts or data scientists," said Forrester principal analyst Jennifer Belissent, who wrote the report. "Data literacy programs must start with basic skills and general awareness of what is data today, how it can be used, the value that it brings to the organization, and their role in collecting and protecting it. That's the only way to ensure that everyone in the organization is doing their part," Belissent added.

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Empowering Industry 4.0 with Artificial Intelligence

Article | February 25, 2020

The next step in industrial technology is about robotics, computers and equipment becoming connected to the Internet of Things (IoT) and enhanced by machine learning algorithms. Industry 4.0 has the potential to be a powerful driver of economic growth, predicted to add between $500 billion- $1.5 trillion in value to the global economy between 2018 and 2022, according to a report by Capgemini.

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Effects of Artificial Intelligence on Software Development

Article | February 25, 2020

What’s the core of those drone-supported Amazon deliveries, online food orders, the ability to watch your favorite shows on Netflix, and virtually augmented monitoring of your upcoming trip to Disneyland? Software! They constitute a significant part of almost every evolution we see around us. But how are the developers managing to yield so much from computer programming? How are they able to enrich so many lives through their creations all over the world? The answer is simple — Artificial intelligence (AI). Undoubtedly, AI is one of the leading technologies now, and it has the power to transform every bit of any business’ functionality. The software industry is not behind in making the most of AI and delivering intelligent and intelligent software. On the contrary, modern enterprises are convinced to adopt an entirely new software development paradigm to stand out from the competition. Traditionally, machine learning was predominant in the Software Development Lifecycle (SLDC). Even though it could encode numerous tasks in a computer program, it took relatively more time to be finalized. It required developers to put the exact requirements together first and hand them over to engineers. And then, engineers programmed the code accordingly. However, AI came with its advantages. As a result, it is reshaping the modern world of automated testing, Agile test software, and ultimately the entire software development. So if you see bots accompanying computer programs to make software development even easier, faster, and smarter in the future, it will be because of AI. So if you are already thinking of potential changes AI will bring to your software development process and how you can reap all the benefits of AI software development, stay tuned! Area of AI Software Development Artificial intelligence has a significant impact on various aspects of software development, for example, software testing, coding, designs, etc. Let’s now discuss what role AI will play in the current and future of software technologies by reshaping the major software development areas. Software Design Process will Improve Designing software is one of the most complicated and error-prone stages of software development. Therefore, specialized skills and the right experience are crucial for designing and planning software development projects to come up with an absolute solution. Moreover, the software designs are mostly subjected to dynamic changes as clients may suggest changes in different stages of software development. AI-powered systems such as AIDA (Artificial Intelligence Design Assistant) can eliminate such complexities in the design process. Time & Money Saving Software Testing Traditionally, software testing takes a lot of time, especially when there are changes in the source code. Plus, it's costly, too! But in the end, it’s one of the essential software development stages as it ensures product quality. Therefore, there’s no room for error. Thankfully, there’s AI and a variety of software testing tools. Testers can utilize them to develop test cases and carry out regression testing. This kind of automated testing is relatively faster, smarter, and astonishingly time and money-saving. On top of all, it's error-free! Easy Data Gathering and Analysis Data gathering and data analysis are the most fundamental stage of any software development lifecycle and need a significant amount of human intervention. The project team has to come up with all the information necessary for the software development, and clients' input can be dynamic. Automated data gathering through various AI tools such as Google ML Kit can be the best option to ease the process. It can take care of specific data-gathering processes without the need for significant human intervention. Say Bye to Manual Code Generation Generating huge codes requires a lot of labor, time, and money. Therefore, simplifying the code generation process is significant because code writing is crucial for any software development life cycle. While traditional code generation can fall short in identifying the target goals effectively, automated code generation can be a game-changer. This is because AI tools typically generate snippets of reusable codes and write code lines as instructed. As a result, they save a substantial amount of money, labor, and time. Benefits of Artificial Intelligence in Software Development Incorporating artificial intelligence in software development can do wonders. Considering the incredible impact of AI on software development and the possibility of incredible transformations in the future software technologies due to AI, here are some promising benefits of AI software development. Enhanced accuracy in estimates Conceptual decision making Error-free end product Easy bugs and error detection Improved data security Conclusion The software development landscape is rapidly changing, and AI has a lot to do with it. Being an enterprise, you need to understand the benefits of AI and how it is enriching human lives worldwide. It's hard to deny the tremendous pressure on the current software development industry from the demand for applications. However, it’s one of the fastest-growing industries, and AI can simplify it with secure, unique, and scalable solutions. Unquestionably, AI software development is the future, and adopting it is the best decision enterprises can make. Frequently Asked Questions What are the things to consider when adopting AI for software development? It would help if you consider the following factors to reach new heights with AI software development: Cloud is necessary for AI AI solutions are much more than implementing machine learning algorithm AI is near real-time or real-time Big data is required for AI Machine learning-powered AI solitons may need frequent retraining What are the real-world examples of integrating AI into software development? Here are some examples of AI tools that several organizations are using for efficient AI software development: Deep Code Stack Overflow AutoComplete Google Bugspot Tool w3C What are the top machine learning and AI tools software developers should consider? Generally, Machine learning software, Deep Learning software, AI platforms, and Chatbots are the four major types of software. Apart from the tools mentioned above, developers should consider the following AI tools for the enhancement of software development: Google Cloud’s AutoML Engine Kite AIDA Testim.io IBM Watson Amazon Alexa Cortana TensorFlow Azure Machine Learning Studio { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are the things to consider when adopting AI for software development?", "acceptedAnswer": { "@type": "Answer", "text": "It would help if you consider the following factors to reach new heights with AI software development: Cloud is necessary for AI AI solutions are much more than implementing machine learning algorithm AI is near real-time or real-time Big data is required for AI Machine learning-powered AI solitons may need frequent retraining" } },{ "@type": "Question", "name": "What are the real-world examples of integrating AI into software development?", "acceptedAnswer": { "@type": "Answer", "text": "Here are some examples of AI tools that several organizations are using for efficient AI software development: Deep Code Stack Overflow AutoComplete Google Bugspot Tool w3C" } },{ "@type": "Question", "name": "What are the top machine learning and AI tools software developers should consider?", "acceptedAnswer": { "@type": "Answer", "text": "Generally, Machine learning software, Deep Learning software, AI platforms, and Chatbots are the four major types of software. Apart from the tools mentioned above, developers should consider the following AI tools for the enhancement of software development: Google Cloud’s AutoML Engine Kite AIDA Testim.io IBM Watson Amazon Alexa Cortana TensorFlow Azure Machine Learning Studio" } }] }

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AI TECH

Artificial Intelligence 2020 Stories: The Great, the Glowing and the Gross Truths

Article | February 25, 2020

2020 has been an unprecedented year where we have seen more downs than ups. COVID-19 has impacted every aspect of our lives. But when it comes to digitisation and Artificial Intelligence, we have seen some impactful developments and achievements. As we approach the end of 2020, it is worth to look back at these AI stories to highlight the truths and discuss what it means for AI future direction. The Great Truth: Artificial intelligence played a crucial role in the detection and fight against COVID-19. Indeed, we have seen the emergence of the use of AI at hospitals to evaluate chest CT scans. With the use of deep learning and image recognition, COVID patients were diagnosed thus enabling the medical team to follow the necessary protocols. Another application was the triage of COVID-19. Once a patient has been diagnosed with COVID, AI has been used to predict the likely severity of the illness so the medical staff can prioritize resources and treatments. COVID has highlighted the need to deploy intelligent autonomous agents. As a result, we have seen both robots used at hospitals to diagnose COVID-19 patients and drones deployed to monitor if the public is adhering to social distancing rules. Another major AI contribution in the fight against COVID-19 is in the area of vaccine and drug discovery. Moderna’s vaccine that has been approved by US Food and Drugs Administration has used machine learning to optimise mRNA sequencing. The above is a proof that AI can make great contribution to mankind if it is used for “good”. The Glowing Truths: Some impressive AI results have been achieved. However, to leap forward a holistic and sustainable approach is needed. 2020 has seen some great AI achievements and leaps forward. The first example is Deepmind’s AlphaFold. The model scored highest at the Critical Assessment of Structure Prediction competition. The algorithm takes genetic information as inputs and outputs a three-dimensional structure. The model has impressively addressed a 50-year-old challenge of figuring out want shapes proteins fold into known as the “protein folding problem”. While Deepmind’s AlphaFold is a great achievement, it is noted by some scientists that it is unclear how the model will work with more real-world complex proteins. Thus, more work is needed in this area. The second example is OpenAI’s GPT3. The model is a very large network composed of 96 layers and 175 billion parameters. The model has shown impressive results for several tasks such as NLP questions & answering and generating code. However, it is noted that the model does not have any kind of reasoning and does not understand what it is generating. Furthermore, its large size makes it very expensive. It is also unsustainable carbon footprint wise; its training is equivalent to driving a car to the moon and back. While both AlphaFold and GPT3 models are both impressive achievements, there are some philosophical challenges/ questions that need to be addressed/ answered. The first question is about games/ simulated worlds vs. real world examples. Most often algorithms/models succeed in simulated world but fail in real world as the environment is more complex. How can we close the gap? How can we make the AI models succeed with complex tasks? I guess the first step is to apply AI to a real-world example with varied complexity levels. The second question is about the structure and the size of AI models. Do models have to be big? Can we come up with a new generation of algorithms/ models that are smaller is size and have more efficient computations? Well to answer this question we have to take a pause on deeplearning and explore new venues. The Gross Truths: Ethics and bias remain the main drawbacks of Artificial Intelligence. Over the last year, we had several prominent examples of AI ethics and bias issues. The first example relates to facial recognition: after several calls against mass surveillance, racial profiling and bias, and in light of Black Lives Matter movement starting in the United States, several tech companies such as Microsoft banned the police from using its facial recognition technology. The second example relates to the use of an algorithm to predict exam results during COVID-19 period: after accusations and protests that the controversial algorithm was biased against students from poorer backgrounds, the United Kingdom government was forced to ditch the algorithm. In the absence of regulations and tightened frameworks, ethics and bias will continue to be the main concerns surrounding the use of artificial intelligence. Looking into the future, AI adoption will continue to accelerate, and we will probably see more breakthroughs achieved by only if we start looking at the subject in a holistic and sustainable view. Focusing models on real world problems and reducing the models carbon footprint will be a major step forward. We need to move away from thinking that “more” is always “more”. Sometimes “more” is “less”.

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