EVOLUTION OF MODELOPS: NOW A MORE ADVANCED ARTIFICIAL INTELLIGENCE

All about Agile, Ansible, DevOps, Docker, EXIN, Git, ICT, Jenkins, Kubernetes, Puppet, Selenium, Python, etc

ModelOps is a set of automated practices and tools that help deploy, manage, monitor, and improve models in production. The approach is designed to be model-centric, which means everything is instrumented around the model, from deployment to governance to inference and monitoring to scale.

Far and wide, investment in artificial intelligence and machine learning are drastically increasing and new data science projects are underway to build predictive and analytical models for various purposes. However, while companies plan to scale up sophisticated Artificial Intelligence solutions in a reasonable time, the harsh reality is that the adoption of these solutions is often stalled because companies generally focus more on development than on the operationalization of the models. On that note, ModelOps comes to the rescue bringing advancements in AI.

 

ModelOps Tools

Since the ModelOps approach brings all the players together, several emerging start-ups, as well as enterprise companies, offer ModelOps solutions to orchestrate these components collectively in an end-to-end fully automated model life cycle. Let us have a look at the figure below showing how by managing a platform enterprises can govern and scale any AI initiatives.

Powerful platforms like ModelOp center typically integrate with development platforms, IT systems, and enterprise applications so that businesses can leverage and extend ongoing investments in AI and IT. In this way, data scientists can work at scale using the tools they know best.

 

ModelOps-

• is primarily focused on the governance and life cycle management of AI and decision models (including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models). Core capabilities include the management of model development environments, model repository, champion-challenger testing, model rollout/rollback, and CI/CD (continuous implementation/continuous delivery) integration
• enables the retuning, retraining, or rebuilding of AI models, providing an uninterrupted flow between the development, operationalization, and maintenance of models within AI-based systems
• provides business domain experts autonomy to assess the quality (interpret the outcomes and validate KPIs) of AI models in production and facilitates the ability to promote or demote AI models for inferencing without a full dependency on data scientists or ML engineers.

 

A More Advanced AI

AI answers Distress and Help-calls

Emergency relief services are flooded with distress and help calls in the event of any emergency. Managing such a huge number of calls is time-consuming and expensive when done manually. The chances of critical information being lost or unobserved are also a possibility. In such cases, AI can work as a 24/7 dispatcher. AI systems and voice assistants can analyze massive amounts of calls, determine what type of incident occurred and verify the location. They can not only interact with callers naturally and process those calls, but can also instantly transcribe and translate languages. AI systems can analyze the tone of voice for urgency, filtering redundant or less urgent calls and prioritizing them based on the emergency.

 

Predictive Analytics for Proactive Disaster Management

Machine learning and other data science approaches are not limited to assisting the on-ground relief teams or assisting only after the actual emergency. Machine learning approaches such as predictive analytics can also analyze past events to identify and extract patterns and populations vulnerable to natural calamities. A large number of supervised and unsupervised learning approaches are used to identify at-risk areas and improve predictions of future events. For instance, clustering algorithms can classify disaster data based on severity. They can identify and segregate climatic patterns which may cause local storms with the cloud conditions which may lead to a widespread cyclone.

Predictive machine learning models can also help officials distribute supplies to where people are going, rather than where they were by analyzing real-time behavior and movement of people.

In addition, predictive analytics techniques can also provide insight for understanding the economic and human impact of natural calamities. Artificial neural networks take in information such as region, country, and natural disaster type to predict the potential monetary impact of natural disasters.

Recent advances in cloud technologies and numerous open-source tools have enabled predictive analytics with almost no initial infrastructure investment. So agencies with limited resources can also build systems based on data science and develop more sophisticated models to analyze disasters.

As with every progressing technology, AI will also build on its existing capabilities. It has the potential to eliminate outages before they are detected and give disaster response leaders an informed, clearer picture of the disaster area, ultimately saving lives.

No-code AI, a subgroup of AI, can automate data science jobs in its own way.

Despite the fact that AI has been a hot issue for at least a decade, there are still barriers to its adoption by organisations. According to a Deloitte report, 40% of businesses believe AI technology and skills are too costly. No-code solutions aid in the democratisation of AI by making it broadly and inexpensively available.

 

What is No-code AI?

No-code AI refers to a subgroup of artificial intelligence that aims to make AI more approachable to the general consumers. To implement AI and machine learning algorithms, no-code AI implies employing a no-code development platform with a graphical, code-free, and typically drag-and-drop interface. With no-coding AI, non-technical individuals can quickly categorise, assess, and create accurate models to make projections.

 

Importance of No-code AI in Businesses

Regardless of the fact that artificial intelligence has been a hot topic for more than a decade, there are still hurdles to its adoption by businesses. As per a Deloitte report, 40% of businesses believe AI technology and expertise is too costly. No-code solutions help to democratise AI by making it widely and affordably available.

Businesses must develop AI models. According to Forbes, 83 percent of companies believe AI is a strategic priority for them right now, yet there is a shortage of data science expertise. In the last 2 years, the demand for AI expertise has more than doubled. Smaller businesses are forced to rely on citizen data scientists to harness AI use cases since technology and financial services giants are consuming 60% of AI talent.

Building AI models (i.e. training machine learning models) takes time, effort, and practise. No-code AI decreases the time it takes to develop AI models to minutes, allowing businesses to quickly incorporate machine learning into their operations.

While interest in no-code AI has begun to grow, according to Google Trends, it is still far less than the amount of individuals interested in learning ML or autoML. Data scientists have not yet been displaced by no-code AI solutions. This is still a very new field. More adoption will be fueled by the maturation and adaptability of current solutions, as well as extensive integrations.

 

Benefits of No-code AI Solutions

Individuals and companies may now experiment with AI and machine learning more easily thanks to No code AI solutions. These solutions assist businesses in swiftly and affordably adopting AI models, allowing their domain experts to benefit from cutting-edge technology.

 

Combine business experience with AI

Data science is still a new discipline, and most data scientists lack business expertise compared to domain specialists. The most common age of responders is 24 and the median is 30, as per a data science study performed by data science challenge platform Kaggle, which is a crowdfunding solution for AI projects. Business users may utilise their domain-specific knowledge and easily develop AI solutions because of no-code solutions.

 

Fast

Writing code, cleaning data, classifying, organising data, training, and troubleshooting the model are all necessary steps in creating unique AI solutions. For individuals who are unfamiliar with data science, this takes much longer. No-code solutions, according to research, have the capability to save development time by 90%.

 

Cost-effective

Savings is among the most evident advantages of automation and no-code solutions. When firms can have their business users develop machine learning models, they require fewer data scientists.

 

Assist data scientists in their work

Requests from other workers shift the emphasis of a data science team that already exists to easy-to-solve jobs. No-code solutions reduce the number of distracting requests by allowing business users to handle them themselves.

 

Businesses Profiting from AI Analytics Data Automation

The device is the result of work that began in 2013 when the business had the concept of creating a machine that could browse the web and GitHub to locate code and other building blocks to generate fresh ideas for problem-solving. A client firm only needs to submit its domain and describe exactly what it wants to optimise to utilise SparkBeyond Discovery.

SparkBeyond has made a trial product on the market available, which has been under development for two years. McKinsey, Hitachi, Baker McKenzie, PepsiCo, Zabka, Santander, Swisscard, Investa, SEBx, Oxford, and ABInBev are among the company’s clients.

A retailer needed to determine where to establish 5,000 new locations in order to optimise earnings, according to one of SparkBeyond’s client success stories. According to Sagie Davidovich, Founder of SparkBeyond, the company used point-of-sale data from the retailer’s current locations to figure out which ones were the most lucrative. It used data from a variety of external sources, including meteorological data, maps, and geo coordinates, to determine profitability. SparkBeyond then put a variety of assumptions to the test, like if three consecutive wet days near rival tales were associated with profitability. According to Davidovich, proximity to laundromats had the strongest correlation with profitability. Consumers, however, have time to shop while waiting for their laundry, which may seem apparent in retrospect but isn’t at all clear at first.

The firm claims to have a one-of-a-kind position in the AI services industry due to its automated creation of predictive models for analysts. After a data scientist has created a hypothesis to test, most AI technologies attempt to aid them in the modelling and testing process.

 

Competitors in Data Automation Field

Data Robot and H20, for example, are two rivals who provide automated AI and ML modelling. However, according to Ed Janvrin, VP and General Manager of SparkBeyond, this sector of auto ML is becoming progressively mass-produced. According to him, SparkBeyond also has an Auto-ML module.

Dataiku and Alteryx are two rivals that assist with data preparation without the need of coding. However, according to Janvrin, these firms do not provide pure, automatic feature recognition. SparkBeyond is developing its own data preparation tools, which will allow analysts to integrate most forms of data, including time series, text analysis, and geographic data, without having to write any code.

SparkBeyond has unofficially supported a total of $ 60 million from funders as of 2013, which was not previously disclosed. The Israeli venture firm Aleph, Lord David Alliance, and others are among the funders.

 

Conclusion

With a streamlined user interface and integrated methodology, the new no-code platform can now find fresh insights and generate predictive models quicker. It tests millions of hypotheses every minute using internal and external data sources to uncover previously unknown drivers of company and scenario outcomes, and it then explains its outcomes in plain English.

ARE VPNS THE NEW CYBERSECURITY TREND IN 2021?

All about Agile, Ansible, DevOps, Docker, EXIN, Git, ICT, Jenkins, Kubernetes, Puppet, Selenium, Python, etc

It’s no secret that even though the Internet has greatly improved our access to knowledge, exchange, dialogue, and entertainment, the way in which Big Data is beginning to be used in our society is causing some distrust on the part of users. For the time being, the only option to protect oneself completely from the siphoning of personal navigation data is the use of a VPN. This is its primary role, but it has a few non-negligible advantages over proxies.

 

Are VPNs the new cybersecurity trend?

Do you know why Virtual Private Networks might be the new cybersecurity trend? They may be the new digital trend of 2021 for all the reasons that will be outlined.

 

Internet access via public Wi-Fi has finally become safer

More people are accessing the Internet through public Wi-Fi. Almost all cafes, restaurants, and hotels in the world offer their customers free Wi-Fi access. Unsecure in most cases, they are used by hackers to spy on network users or steal their private information such as banking details, social security information, photos, etc.

With a VPN service, you can make it almost impossible for a hacker to access your computer or get bombarded with a denial-of-service attack. In fact, the number of denial-of-service attacks has dropped significantly in recent years due to the increasing use of VPNs.

 

Compatible with all known platforms

In 2021, you don’t have to worry about accessing the Internet on your laptop, iPhone, Samsung, or any other device that can connect to the Internet. VPNs develop applications for every type of device and adapt to all types of OS. One subscription is enough to protect all your equipment.

 

Better online privacy

Most of what people do on the internet does not need to be hidden. But what if you want to access something a little more sensitive? What if you are looking for information about a disease, an emotional problem, or a taboo subject in your country? Letting your Internet Service Provider know which adult websites you visit is not ideal either. Suddenly, the idea of a little privacy is more important.

 

Net neutrality is no more

Nowadays, all ISPs are supposed to treat all traffic to all websites the same. What is happening now is that they can restrict your access to certain streaming sites. A streaming VPN bypasses this type of setback. Also, since Internet Service Providers could monitor and even track every word you type, it is best to access the Internet with a VPN for better Internet security. This completely hides your online activity, and your ISP can no longer track you.

 

Telecommuting is coming of age

Since Covid-19, there has been a major turning point in the way we work. The exceptional containment measures taken to contain the spread of pandemics have resulted in many employees being forced to work from home. In the space of a week, thousands of people suddenly found themselves working from home without having been previously prepared and without equipment in most cases.

The use of a VPN has been democratized in the whole world. If at first, equipping its staff was one of the priorities of companies, the issue of security of exchanged professional data has quickly become a new concern.

A VPN to bypass geo-restrictions

Finally, there is also the problem of geolocation. Some content can be blocked in certain countries, for political or commercial reasons. Hence, a classic problem that may occur because of this situation might be the restriction of access to local content on a streaming service while you are traveling.

Unfortunately, licensing issues and other geolocation restrictions mean you cannot access what you want when you are overseas. A VPN allows you to bypass these types of restrictions.

 

The proxy, the false good idea

If you think you have good digital hygiene because you use a free proxy that guarantees encryption of your data, you should know that the level of encryption is not the same as the level of security. You should know that the level of encryption is not comparable to that of a VPN. You will also notice the lack of intuitive and functional dedicated applications. This also applies to routers. Moreover, proxy servers are easily hackable.

 

Secure connection to public Wi-Fi

Now, we will talk about the issue of public Wi-Fi hotspots. In many countries, Wi-Fi access is available everywhere and often for free. Whether it is a school or university campus, a shopping mall, or coffee shops and restaurants, getting online is easier than ever. However, many of these free Wi-Fi hotspots are completely open and without any encryption.

 

Why are VPNs the new 2021 trend?

Internet security and privacy are major concerns today, therefore VPNs became a new trend. After all, like many people, you keep a lot of personal information on your phones and computers. If privacy is important to you, you should use a Virtual Private Network. It is a term you have probably heard in the context of privacy, and maybe even geolocation. To understand how useful VPNs are, you can try Surfshark.

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3 steps businesses can take to reduce bias in AI systems

Experts have proposed the prioritization of humans faced with technological advancement by working on three key areas.

• Artificial intelligence constitutes one of the most impactful developments for businesses and organizations in general.
• However, this fast-paced and unstoppable trend raises ethical issues.
• It can be challenging to ensure that AI development is fair when the algorithms at its core are designed with racist, sexist, or other biases which are often unconscious.
• Below, Lorena Blasco-Arcas and Hsin-Hsuan Meg Lee propose a human-centred view for the design of specific frameworks and regulatory systems.

“Okay, Google, what’s the weather today?” “Sorry, I don’t understand.”

Does the experience—interacting with smart machines that don’t respond to orders—sound familiar? This failure may leave people feeling dumbfounded, as if their intelligence were not on the same wavelength as the machines’. While this is not the intention of AI development (to interact selectively), such incidents are likely more frequent for “minorities” in the tech world.

The global artificial intelligence (AI) software market is forecast to boom in the coming years, reaching around 126 billion US dollars by 2025. The success of AI technology is forcing many existing companies to transform their business model and shift to AI. However, along with the advance, there is an increasing worry about the biases in the algorithm development of all these tools.

How AI flaws become apparent

Algorithmic bias is nothing new. However, to date, engineers have focused more on developing AI algorithms to solve complex problems than on monitoring and reporting the potential issues these technological advances bring. We have already seen examples of failures by technology with the rise of discriminatory practices. For instance, in 2016, Microsoft released its self-learning chatbot Tay on Twitter. It was supposed to be an experiment in “conversational understanding.” The AI tool could learn language fundamentals, and, over time, it could participate in a conversation by itself. However, the bot ended up developing racist and sexist traits on social media. Another example occurred at MIT when while working on facial recognition, Joy Buolamwini conducted a discriminatory experiment without knowing it. As a dark-skinned woman, she was not recognised by the AI as precisely as her white friend. The results completely missed the point of the experience, and she found out that 99% of white women were identified by the computer, compared to 65% of black women.

Was human intention behind the AI’s behaviour? Maybe, maybe not. These examples do not mean that the AI tools were fundamentally flawed or designed to be racist. Nevertheless, their design was biased, and they were not controlled enough before going public. Data biases can lead to discriminatory practices stemming from human intention or an unintended act, perpetuating generations’ bias(es). Worse still, because the resulting discrimination is almost always an unintentional emergent property of the algorithm’s use rather than a conscious choice by its programmers, it is tough to identify the source of the problem or explain it to a court. Machines tend to give the – false – impression that they are neutral.

How to develop an ethical and non-biased AI application in an undoubtedly biased and unbalanced society? Can AI be the holy grail by developing more balanced societies that overcome traditional inequality and exclusion? It is too early to say, and it seems apparent that we will witness many trial-and-error phases before achieving a consensus on what and how AI might be used ethically in our societies. Much like institutional racism, which requires fundamental shifts in the overall ecosystem, the problems in AI development also call for a similar change to create better output. To solve this issue, we propose to prioritise humans faced with technological advancement by working on three areas:

1. Unbiasing (biased) human beings

Behind the development and implementation of algorithms, there are developers and specific people in power positions. As seen in the data, the developer’s professional world is far from being diverse today, which explains some of the thinking logics that foster biases. Increasing the diversity of and access to developer positions in the big companies that dominate the industry would offer a more critical perspective on how algorithms are developed. This would increase human inclusion rather than the opposite. Suppose we understand algorithmic bias as imposing specific ideas using computers and math as an alibi. In that case, we can question the institutional logic behind the perpetuation of bias and discriminatory practices.

There is a need to increase control, monitoring systems, regulation and common ethical frameworks to ensure that human bias does not permeate the creation and development of algorithms. We echo the view of professors Ayanna Howard and Charles Isbell at Georgia Tech that recognising the importance of diversity in terms of data and leadership, and demanding accountability in certain decisions are essential guiding principles toward achieving a more just development and implementation of AI in the future.

2. Data for good instead of data for bias

Vital initiatives are developing that might help solve historical dataset biases, such as the one carried out by a researcher at the University of Ontario, who used the MNIST dataset and distilled that database of 60K images down to only 5 to train an AI model. Should these procedures be successfully applied to different contexts, they will make AI more accessible to companies that may not afford massive databases. It will also improve data privacy and data collection, as less information from individuals will be required to train relevant models.

3. Educating citizens in the advantages and risks of AI applications

AI development poses diverse and notable challenges concerning understanding societies, politics, business and even our daily lives as citizens As AI becomes increasingly present in business processes affecting individuals’ choices and possibilities, more education is needed to raise awareness and understanding of these topics.

The technology readiness of citizens will improve AI adoption and positively impact the critical assessment of AI implementation and its effects. A more aware citizen will be less tolerant of manipulation and acceptance of biased or unfair applications of AI tech, such as those related to surveillance that might conflict with civil liberties and rights.

Minimising bias in AI is essential to building trust.

Image: McKinsey&Company

Making machines more human, or even suppressing human intelligence, has often been treated as one of the ultimate goals of technological advancement. Human-centred technology development implies that the developers and companies using the machines should not only aim for innovation but also pay attention to their potential impact on society. Humans are flawed, meaning our society is naturally full of biases that are systematic and institutional, and we are not always aware of them. But we should avoid replicating the same issues in the machines we build.

The Hidden Second Face of Deepfakes

A lot of times when you read about deepfakes (more professionally known as synthetic media) the common themes being explored is only one of the two faces of deepfakes the negative side, however, I want to explore some of the positive things deepfakes can be used for so you can get a full scope of the capabilities of deepfakes.

Well, what’s a deepfake anyways? Glad you asked, simple answer: artificial intelligence-generated media that has seamlessly stitch anyone in the world into a video or photo they never actually in and a summarised more technical answer: deepfakes are made by using a GAN (generative adversarial network) a type of deep learning artificial intelligence. It uses two neural networks that rival each other to generate a synthetic version of data that can pass for real data, one of the neural networks is called the generator (generates new data instances) and the other is called a discriminator (evaluates them for authenticity). The purpose of the generator is to generate synthetic media that is given to the discriminator, which its purpose is to identify whether the media is fake or real, they are trained together until it achieves acceptable accuracy (discriminator fooled 50% of the time). A great more visual and detailed explanation of GANs can be found in the video below:

So now that we have a better understanding of how deepfakes are created we can begin to explore the positive uses of deepfakes. In 2019, for a UK-based charity called Malaria Must Die, the company Synthesia used deepfake voice technology in a video in which David Beckham appealed to the end to malaria in nine different languages with pretty convincing lip-sync. Synthesia also proved helpful during the pandemic as it helped advertising company WPP to create training videos without the fear of spreading the disease.

A pretty incredible use of deepfakes within the medical field is the research by Mayo Clinic and the MGH & BWH Center for Clinical Data Science that used deepfake technology to create “fake” brain scans based on actual patient data. These fake scans are then used to train algorithms to spot tumours in real images.

Another positive use of deepfakes is in law enforcement to protect real victims of child sex abuse while catching sexual predators. Earlier this year Germany passed legislation allowing the use of deepfakes to rid the world of online predators. However, there is a real conversation to be had about the ethics of the use of the technology in this regard.

On a lighter note, cool uses of deepfakes would be personal avatars that can allow people to try different clothes and hairstyles without fully committing, just imagine you don’t know whether the fringe you have always wanted will look good on you could use the technology to help your decision.

Although these are some incredible uses of deepfakes there is no denying the dark cloud that will always loom over deepfakes, with stories coming out every week about the dangerous uses of deepfakes. With proper and heavily enforced regulations, the technology could truly benefit society.

Thumbnail Credit: https://www.arm.com/blogs/blueprint/arm-ai-trust-manifesto