Emerrrrrging Tech in Simple Words: February Edition
Andrei Klubnikin ??
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What is AI engineering, and how can your company benefit from it? Could artificial intelligence help physicians distribute and administer COVID-19 vaccines faster? What does it take to move enterprise applications and data to the cloud?
We've got the answers.
What is: AI vs. Machine Learning vs. Deep Learning
In the What is section, we lay out the difference between confusing tech terms and concepts. This time, we’ll focus on AI subsets.
Artificial intelligence is an umbrella term that describes machines' ability to act and think like humans. This term is often used interchangeably with other buzzwords like machine learning and deep learning — particularly in the business context. This is not quite correct.
- Artificial intelligence is a field of science that pursues the goal of creating intelligent machines. Any device or application that feeds off data and attempts to mimic human reasoning can be described as AI
- Machine learning is a subset of artificial intelligence. The ML concept revolves around training algorithms on labeled or unlabeled data like images of cats and dogs. A fully trained algorithm can analyze not only the data it’s been fed on but also unfamiliar and unstructured data information. There are several types of machine learning algorithms, such as supervised, unsupervised, and reinforcement learning models
- Unlike linear ML algorithms, deep learning relies on artificial neural networks modeled after a human brain. Deep learning networks contain multiple hidden layers which process information and assign weights to it. By matching the combined weight against a threshold defined by an AI developer, the algorithm evaluates the probability or improbability of an event
Check out our latest blog post to learn more about artificial intelligence types and subsets, as well as their applications in business.
What's Hot: AI Engineering
In the What’s Hot section, we zoom in on strategic trends and transformative technologies that will define 2021.
Gartner reckons 53% of enterprise AI projects never get past the prototype stage. The disappointing results can be attributed to the lack of artificial intelligence engineering skills among companies undergoing digital transformation.
Experts define AI engineering as a potent mixture of DevOps, ModelOps, and DataOps. The concept revolves around pushing newly developed AI applications straight into a company's DevOps pipeline and fostering collaboration between AI developers, QA teams, and operation specialists. With AI engineering, companies get an opportunity to build, test, and deploy AI-powered products faster while ensuring their scalability, reliability, and interpretability.
If you have questions about AI engineering applications and best practices, feel free to contact the ITRex AI and Data Science Team.
What's New: a Rundown of Top Tech News from All over the Web
What’s New is a collection of technology articles curated for you by our content writers.
AI makes mistakes on purpose to better interact with humans
When IBM's Deep Blue defeated a reigning world chess champion a quarter of a century ago, we realized that AI's sole purpose was to destroy humans — either on a chessboard or on the factory floor.
Led by professor Jon Kleinberg, a team of researchers from Cornell University invented Maia — a whole new type of artificial intelligence that aims to understand human fallibility. For the time being, the scientists leveraged Maia to mimic the logic of amateur human chess players and predict their moves, including the wrong ones. Professor Kleinberg sees Maia's potential stretching far beyond board games. In healthcare, for instance, the algorithm could work alongside human radiologists to detect problematic medical images on which doctors tend to disagree.
Unlike most existing AI programs, Maia was explicitly designed to assist humans and learn from them, which is the go-to approach for the transition period when humans and machines will be collaborating extensively.
AI refactors legacy apps to run on modern platforms
Earlier this month, IBM has unveiled new AI tools that bring software engineers one step closer to automatically modernizing legacy IT systems. Dubbed as Application Modernization Accelerator (AMA) and Mono2Micro, the tools spot hidden connections in old applications, group similar code patterns together, and refactor monolithic applications into standalone microservices.
Although IBM has yet to teach AMA and Mono2Micro to actually translate code written in PL/I or COBOL into modern languages, the introduction of AI-driven code modernization tools marks a new chapter in software engineering and could sonically improve developers' ability to update legacy systems faster.
Smart algorithms infiltrate our lives, promise unheard-of personalization
Unless you've been living under a rock for the last ten years, chances are you watch TV shows recommended by Netflix engines, orchestrate your home appliances using Alexa, and let Wix algorithms parse your social media data to create websites that truly reflect your brand's identity.
Having said that, researchers still compare the current state of artificial intelligence to the cumbersome cellphones of the 90s. As AI matures, we're bound to see more personalized interactions with technology.
Chatbots will soon become genuinely conversational. Ambient intelligence solutions will be watching over senior citizens at home and alerting their caregivers in emergency situations. And realistic 2D avatars of tutors will inevitably populate virtual and physical classrooms.
Check out the full story by The New York Times to get a glimpse into our bright AI future.
Californian startup taps into RPA to improve COVID-19 vaccine administration
As major states' vaccination levels still fluctuate around 15%, it's become clear we need a better strategy to distribute and administer COVID-19 vaccines to patients across the US.
Notable, a Californian startup that automates doctor-patient interactions, hopes to achieve the feat with the help of its AI-based robotic process automation (RPA) platform. According to Muthu Alagappan, Notable's Medical Director, the platform will harness RPA, machine learning, and natural language processing to analyze digital health records and identify patients eligible to receive the vaccine. The system will then provide citizens with comprehensive, trustworthy information about the vaccine and set up appointments with their physicians.
Ultimately, the smart approach could help healthcare providers kill two birds with one stone — i.e., debunk misinformation about COVID-19 vaccines and assist patients in navigating health websites, which are seldom built with UX in mind.
How to: Sail through Cloud Migration
In the How to section, our tech experts provide practical, easily applicable tips to accelerate your company’s digital transformation.
When done right, cloud migration promises a significant reduction in physical server costs, an extra layer of security for your applications and data, and greater flexibility in business workflows. If you haven't moved your company's digital assets to the cloud yet, you're already lagging behind 90% of your competitors.
The sobering truth about cloud computing is that 30% of companies fail their migration projects.
To help our clients reap the full benefits of cloud computing, we set down to talk with Alexey Zhadov and Michael Pranovich, our top cloud experts. The ITRex team came up with an actionable plan to make your cloud migration a cakewalk:
- Start your cloud journey with a strategic business case, like saving money on hardware upgrades, supporting a larger base of application users, or speeding up software updates
- Assign a skilled business analyst to your project to determine what business value you can glean from the cloud and translate your business goals into technical requirements
- Conduct a thorough audit of your current IT infrastructure to measure your technical debt, figure out what apps you're using actively, and determine how those apps talk to each other. Also, it is crucial to evaluate your spending on physical servers, the amount of data generated by your company and storage needed, as well as your analytics and networking requirements
- Identify data and applications that require additional protection from cybercriminals
- Decide which apps or their portions can be migrated as they are, which can be easily made cloud native, and which should be rewritten entirely. As a next step, select the right environment — e.g., IaaS or PaaS — for your cloud deployment
- Keep your business goals in mind when selecting a cloud computing provider. In most cases, it makes sense to utilize several cloud computing services to avoid vendor lock-in
- Bring in external experts to fill the knowledge and skill gaps in your IT department
- Move your applications to the cloud one at a time and learn from your mistakes in the process
- Validate that everything works as expected before moving the next portion of the software to the cloud. Comprehensive testing makes it much easier to deal with any issues arising without grinding your systems to a halt
- Turn to automation to provision environments, convert your legacy code, conduct security and performance checks, and roll out new features and security updates
That's all for today. Make sure to follow ITRex Group on LinkedIn and Medium to keep up with the IT industry trends and get practical tips from AI, QA, cloud computing, and intelligent automation experts.
See you in March!
Cheers,
Andrei Klubnikin and the ITRex Content Team
