Artificial intelligence is playing a vital role in processing the big data sets of industrial companies.

Artificial intelligence is being used by large industrial companies to analyze their long array of unstructured datasets and put them into smart use. AI is creating analytics models that are creating accurate operating strategies based on variables like pump speed or weather. To be successful in this process, the big industries must know how to create an amenable environment for AI to work properly with their big datasets.

The Making of Smart Data

There is a five-step approach that can be adapted to process the big datasets into smart data. First of all, the steps of the process must be outlined, along with addressing the physical and chemical changes like grinding, heating, oxidation, and polymerization. The process flow of the operation should be labeled using paint schematics or engineering drawing. In the next step, the non-standard operating regimes should be removed. A common data science approach should be used to engineer input combinations to produce new features. When combined with the sheer number of sensors available in modern plants, this demands a massive number of observations. Instead, teams should prepare the features list to include only those inputs that describe the physical process, and then they should apply deterministic equations to create features that intelligently combine sensor information.

The sensor calibrations should be addressed and a high-quality dataset should be built. The next phase of the process would be to leverage the engineering formulas to combine the sensor data in an intelligent manner.

In the next step, advanced analytic models should be overlaid on engineered data for capturing the stochastic variability. Teams should evaluate features by inspecting their importance and therefore their explanatory power. Ideally, expert-engineered features that capture, for example, the physics of the process should rank among the most important. Overall, the focus should be on creating models that drive plant improvement, as opposed to tuning a model to achieve the highest predictive accuracy. Teams should bear in mind that process data naturally exhibit high correlations. In some cases, model performance can appear excellent, but it is more important to isolate the causal components and controllable variables than to solely rely on correlations. The last step includes checking casualties and ensuring the facts that the results are physical.

The Making of Analytics Team

The team responsible for the implementation of AI must have a variety of members from operators to data scientists, automation engineers, and process experts. Companies that are looking to implement AI generally need to rebuild their expert pipeline initially. Knowing the skills is the most important factor when it comes to choosing the perfect process expert. Planning out the model development can be a good exercise to solidify a way of working and avoid linear approaches that include exhaustively completing one stage before proceeding to the next. Later the team can decide what to invest in for the next stage.

Industrial companies are looking to AI to boost their plant operations, reduce downtime, proactively schedule maintenance, improve product quality, and so on. However, achieving operational impact from AI is not easy. To be successful, these companies will need to engineer their big data to include knowledge of the operations. The cross-functional data science teams should include employees who are capable of bridging the gap between machine learning approaches and process knowledge. Once these elements are combined with an agile way of working that advocates iterative improvement and a bias to implement findings, a true transformation can be achieved.

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In March 2021, Google announced that it was ending support for third-party cookies, and moving to “a more privacy first web.” Even though the move was expected within the industry and by academics, there is still confusion about the new model, and cynicism about whether it truly constitutes the kind of revolution in online privacy that Google claims.

To assess this, we need to understand this new model and what is changing. The current advertising technology (adtech) approach is one in which platform corporations give us a “free” service in exchange for our data. The data is collected via third-party cookies downloaded to our devices, that allow a browser to record our internet activity. This is used to create profiles and predict our susceptibility to specific ad campaigns.

Recent advances have allowed digital advertisers to use deep learning, a form of artificial intelligence (AI) wherein humans do not set the parameters. Although more powerful, this is still consistent with the old model, relying on collecting and storing our data to train models and make predictions. Google’s plans go further still.

Patents and plans

All corporations have their secret sauce, and Google is more secretive than most. However, patents can reveal some of what they’re up to. After an exploration of Google patents, we found U.S. patent US10885549B1, “Targeted advertising using temporal analysis of user-specific data”: a patent for a system that predicts the effectiveness of ads based on a user’s “temporal data,” snapshots of what a user is doing at a specific point instead of indiscriminate mass data collection over a longer time period.

We can also make inferences by examining work from other organizations. Research funded by adtech company Bidtellect demonstrated that long-term historical user data is not necessary to generate accurate predictions. They used deep learning to model users’ interests from temporal data.

Alongside contextual advertising — which displays ads based on the content of the website on which they appear — this could lead to more privacy-conscious advertising. And without storing personally identifiable information, this approach would be compliant with progressive laws like the European Union’s General Data Protection Regulation (GDPR).

Google has also released some information through the Google Privacy Sandbox (GPS), a set of public proposals to restructure adtech. At its core are Federated Learning Cohorts (FLoCs), a decentralized AI system deployed by the latest browsers. As the Google AI blog explains, federated learning differs from traditional machine learning techniques that collect and process data centrally. Instead, a deep learning model is downloaded temporarily onto a device, where it trains on our data, before returning to the server as an updated model to be combined with others.

With FLoCs, the deep learning model will be downloaded to Google Chrome browsers, and analyze local browser data. It then sorts the user into a “cohort,” a group of a few thousand users sharing a set of traits identified by the model. It makes an encrypted copy of itself, deletes the original and sends the encrypted copy back to Google, leaving behind only a cohort number. Since each cohort contains thousands of users, Google maintains that the individual becomes virtually unidentifiable.

Cohorts and concerns

In this new model, advertisers don’t select individual characteristics to target, but instead advertise to a given cohort, as Google’s Github page explains. Although FLoCs may sound less effective than collecting our individual data, Google claims they realize “95 per cent of the conversions per dollar spent when compared with cookie-based advertising.”

The bidding process for ads will also take place on the browser, using another system codenamed “Turtledove.” Soon, Google adtech will all work this way, contained on a web browser, making constant ad predictions based on our most recent actions, without collecting or storing personally identifiable information.

We see three key concerns. First, this is only part of a much larger AI picture Google is building across the internet. Through Google Analytics, for example, Google continues to use data gained from individual website-based first-person cookies to train machine learning models and potentially build individual profiles.

Secondly, does it matter how an organization comes to “know” us? Or is it the fact that it knows? Google is giving us back legally acceptable individual data privacy, however it is intensifying its ability to know us and commodify our online activity. Is privacy the right to control our individual data, or for the essence of ourselves to remain unknown without consent?

The final issue concerns AI. The limitations, biases and injustice around AI are now a matter of widespread debate. We need to understand how deep learning tools in FLoCs group us into cohorts, attribute qualities to cohorts and what those qualities represent. Otherwise, like every previous marketing system, FLoCs could further entrench socio-economic inequalities and divisions.

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The population ageing, changing patient expectations, a shift in lifestyle choices, and the never-ending cycle of innovation are a few of the implications of an ageing population.

New Delhi: Medical science has come a long way from what it was back in the 80s and 90s. With the new era and technology, the healthcare industry is at its best times and with the growing demand is an unstoppable industry to look forward to. Read on to know what is really building on!

The population ageing, changing patient expectations, a shift in lifestyle choices, and the never-ending cycle of innovation are a few of the implications of an ageing population. As per the joint report with the European Union’s EIT Health, by 2050, one in four people in Europe and North America will be over the age of 65—this means the health systems will have to deal with more patients with complex needs. Managing such patients is expensive and requires systems to shift from an episodic care-based philosophy to one that is much more proactive and focused on long-term care management. What will come as an aid to the industry are artificial intelligence (AI), machine learning (ML) and deep learning (DL) as they will help revolutionize healthcare and address some of the major challenges.

So, what is AI? It is the capability of a computer program to perform tasks or reasoning processes that we usually associate with intelligence in a human being. AI can lead to better care outcomes and improve the productivity and efficiency of care delivery. It can also improve the day-to-day life of healthcare practitioners, letting them spend more time looking after patients and in so doing, raise staff morale and improve retention. It can even get life-saving treatments to market faster. Artificial Intelligence in Healthcare is expected to grow from $2.1 billion to $36.1 billion by 2025, displaying a CAGR of 50.2% over the span.

What really is ML? ML is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. Machine learning (AI) has indeed played a key role in many areas of health care, including the development of new medical procedures, the handling of patient data and records, and the treatment of chronic diseases. It is understood that hospitals, clinics, and other healthcare organizations around the globe are gradually beginning to recognize the need for digitization and integration within administrative processes. In recent years, scientists and scholars have joined the field of cancer diagnosis and treatment. One future approach is combining cognitive computation with genomic tumour sequencing. This uses machine learning to build diagnostics and clinical therapies. For example, The da Vinci robot helps surgeons to conduct procedures at a level of precision. These robotic hands are more precise and reliable than human hands. Computer vision and machine learning are used to classify the body parts of humans.

Moving to DL, here is what it really means and how it is helping the healthcare industry. Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. When it comes to healthcare, in a recent book published by Dr Eric Topol entitled ‘Deep Medicine’, the cardiologist and geneticist emphasize how deep learning in healthcare could ‘restore the care in healthcare. Aidoc, for example, has developed algorithms that expedite patient diagnosis and treatment within the radiology profession. The company has received several accreditations and approvals from the Food and Drug Administration, the European Union CE and the Therapeutic Goods of Australia (TGA) for its specialized algorithms. These algorithms include intracranial haemorrhage, pulmonary embolism and cervical-spine fracture and allow for the system to prioritize those patients that are in most need of medical care. This targeted form of AI and deep learning helps the overburdened radiologist by flagging items that are of concern and thereby allows the healthcare professional to direct patients with greater control and efficiency. It also reduces admin by integrating into workflows and improving access to relevant patient information.

AI is now top-of-mind for healthcare decision-makers, governments, investors and innovators, and the European Union itself. An increasing number of governments have set out aspirations for AI in healthcare, in countries as diverse as Finland, Germany, the United Kingdom, Israel, China, and the United States and many are investing heavily in AI-related research. The private sector continues to play a significant role, with venture capital (VC) funding for the top 50 firms in healthcare-related AI reaching $8.5 billion, and big tech firms, startups, pharmaceutical and medical-devices firms and health insurers, all engaging with the nascent AI healthcare ecosystem.

We are in the very early days of our understanding of AI and its full potential in healthcare, in particular with regards to the impact of AI on personalization. Nevertheless, interviewees and survey respondents conclude that over time we could expect to see three phases of scaling AI in healthcare, looking at solutions already available and the pipeline of ideas.

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It shows so much wisdom when people say it is not easy for you to manage things you cannot measure. The recent development of digital data is now very important for businesses. With the help of big data, managers can now measure and know more about their business performance because this knowledge will help them to make informed decisions.

The revolution of big data comes with its challenges. Businesses need to learn how to secure their data. Nevertheless, it is a welcome decision that business managers need to leverage.

What is new about big data?

Managers often ask “is big data the same thing as analytics?” The truth is that there is a relationship. The big data evolution, just like analytics, seeks to obtain information and knowledge from data. Apart from these, there are three main differences:

1. Velocity

Data is being created in no time. The process is fast, and you gain rapid insights quickly. These insights help business managers to gain a competitive advantage.

2. Volume

More data now cross the internet every second. The number keeps doubling every 40 months and the story is no longer the same. Big data allows many companies to access many petabytes of data daily.

3. Variety

Big Data is usually in from of GPS signals from phones, images on social networks, updates, messages, and many more. As more businesses go digital, there are newer sources of information from cheaper equipment. Managers no longer use intuition; they now make decisions based on evidence. Variety is the spice of big data. Electronic communications, social networks, mobile phones all produce large amounts of data from their normal operations. We all use them daily, we are now walking data-generators.

Benefits of big data in business

1. It delivers a competitive advantage

Data is a huge part of a modern workplace. Corporations have been able to move forward and far ahead of their peers. A recent survey shows that organizations that use advanced analytics have been able to move ahead of their counterparts. Such companies have been able to drive revenue twice as that of their industry peers. Not only that, but they are also likely to make the best decisions than their market peers.

2. Improvement in decision making

Companies can now back up their decisions with evidence rather than intuition and emotions. It provides a balance where opinions vary. With big data, you can review your products better. 76% of people are likely to purchase a product like the detox kit after seeing its review.

Analytics engines are smarter and unbiased. They can absorb a company’s outcome and connect them with relevant data. The best aspect is, they analyze these data without limitations that human decision-makers experience.

3. Improvement in performance

Big data drive productivity. Analyzing individual and team performance will show business managers the necessary areas for improvement. Instead of conducting a one-time training for employees, the use of big data will continually reveal new areas that need improvement.

Challenges of big data

1. Leadership

Companies that use big data still need leadership teams that can set realistic goals and ask the right question. Big data still need human insight and vision to drive success. The successful companies in the next decade will be those who have good leaders. These leaders will be the ones to understand the market, deal with customers, and also think creatively.

2. Talent management

Data scientists are the most crucial to this revolution. Many of the techniques for using big data are rarely taught in schools. People with these skills are hard to find, and they are currently in high demand. Only data scientists can manipulate and understand big data sets while making sense of the results in management and business terms.

3. Security and data privacy

There are potential risks in terms of security and privacy. Tools used for data analysis store the data in disparate sources. This leads to exposure of data, making it vulnerable to breach. The more data you get, the deeper are your privacy and security concerns.

Companies should make use of Data Lake to store a vast amount of data. Data Lake is different from a traditional warehouse. It protects data from unwanted manipulation and enables businesses to make decisions accurately.

Conclusion

Despite its challenges, it is clear that data-driven companies make better decisions than their industry peers. Companies that figure out how to use it effectively will move ahead of their rivals. Most importantly, there is now an exponential growth in the amount of data. Businesses need to leverage the combination of data, the right people, and tools to drive change.

Source: Big Data: The Data Management Revolution

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Microservices represent a radical shift in how organisations approach application development

The world of enterprise software development came of age with the emergence of ‘single purpose’ software applications aligned to a specific business function. It started with accounting programmes in finance, but with time, many areas such as manufacturing, supply chain and inventory management also benefited from the emergence of purpose-built applications.

Monolithic structures like ERPs, for instance, were designed to increase efficiency by transmitting information across business functions. Problems started creeping in, however, when businesses customised these applications to cater to their own unique requirements.

More often than not, increased customisation rendered these applications slow and clunky since they were too rigid to scale, making frequent iterations difficult. The IT department that was supposed to incite productivity became the reason for falling behind.

From a software lifecycle management perspective, monoliths carry larger risk than smaller applications. Implementing, updating and maintaining these applications can be a daunting task since there are so many moving parts that require constant attention. Microservices enable businesses to overcome this challenge.

Rewiring software architectures

In short, the microservices approach to software development means building applications with lots of smaller, modular parts – allowing enterprises to create software that’s more agile and independently scalable.

For example, if a business is shifting a monolith application to the cloud, it means relocating the same clunky software architecture on a separate system, along with all the shortfalls. This is why it’s important to build nimble and agile applications that can engage customers quickly and meet demands as soon as they appear.

A recent survey reveals that 63 percent of companies are currently using microservices architecture. A further 60 percent are doing so to attain faster turn-around times for new services and products, and 54 percent to drive digital transformation and next-gen applications.

What’s more, it’s easy to see why.  Microservices allow companies to deploy application functionalities as discreet lightweight services, which interact with businesses through application programme interfaces (APIs).

This allows businesses to deliver small application changes incrementally while speeding up delivery and reducing service disruptions, since if something needs changing, one ‘module’ can be removed rather than having to rewire the whole application. Considering that mobile and other digital applications are extremely dynamic and require frequent updates, microservices prove to be extremely effective there too.

This said, microservices architecture doesn’t entail combining several software components together. Rather, it involves the seamless integration of independent application functionalities that can communicate with each other through APIs. These interfaces allow enterprises to escape monoliths, as they serve as a ´contract’ between microservices.

To simplify their transition from a monolithic to microservices architecture, enterprises need a roadmap in place, specifically an ‘A-B-C’ approach:

• Abstract: Creating a layer of abstraction (or API layer) to access the capabilities required to service the customers, employees, partners, and machines
• Build: Aligning these capabilities to improve user experience, while separating the experience from how systems are architected
• Change: Breaking down the back-end services to more manageable microservices

The challenges that come with adoption – and how to overcome them

This simplified path to adoption may mean microservices is poised to become the default model of software lifecycle management going forward. However, according to a survey from Lightstep, a whopping 99 percent of organisations have reported challenges when adopting microservices.

For one, for microservices to perform at its full potential in terms of speed and consistency, continuous integration, testing, and delivery processes are required. One way to overcome this would be to deploy a service virtualisation strategy. This can help developers and testers to quickly simulate testing environments (even if the production environment is complex), reduce dependencies and allow ease of integration.

Also, with a microservices architecture that’s driven by APIs, organisations might have to keep track of hundreds of services running simultaneously. In such circumstances, monitoring even the smallest of changes in an application is tough, which is why developers should embed telemetry and analytics into the platform to simplify operations and change management.

Finally, it’s vital that every team involved in the microservices value chain takes responsibility for securing the services. Ensuring that calls are always routed through a secure service API gateway helps establish consistent security policies.

In short, teams developing microservices should care just as much about ensuring quality, operating and securing the software as much as developing it.

Time for change

Software architecture design might not appeal to all decision-makers across the board, but whether they like it or not, software applications now lie at the core of how a business operates. Their nimbleness, overall performance and resilience directly impacts business agility and ultimately revenue.

Microservices represent a radical shift in how organisations approach application development while moving to a software-centric model. If one’s thing’s for sure, it’s that it’s time for businesses to start exploring its potential to redefine the services they deliver to their customers.

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The 4th industrial revolution is upon us, and it is time to adjust your portfolio for the next wave of tech that will run our future economy.

The 1st industrial revolution was characterized by mechanization led by water power and the steam engine. The 2nd was mass production powered by electricity driven by oil-based power. The digital revolution or 3rd industrial revolution started 50 years ago, which has shaped the computerized world we live in today. Now, this is coming to an end as the 4th industrial revolution takes form.

This 4th industrial revolution is building on the digital revolution emphasizing intelligence and information. Big data, cloud computing, and the illustrious artificial intelligence are going to drive the world economy as technology and information proliferate.

Big data analytics is becoming crucial for any business to remain competitive in today’s economy. The need for this kind of analytics is going to escalate as the technology behind it becomes increasingly useful. I discussed AI in my previous article: The 4th Industrial Revolution Is Upon Us: Prepare Your Portfolio. In this article, I will discuss some big data stocks that are worth exploring for your portfolio.

Alteryx AYX

AYX has shown remarkable returns to anyone lucky enough to get into these shares before today. Since Alteryx went public 2.5 years ago, it has driven 817% share price appreciation, and just this year the stock has grown an astounding 139% with investors rushing to get into this exciting big data player.

Alteryx provides data analytics and solutions for 5,278 customers in more than 70 countries, serving some of the biggest corporations in the world, including more than 1/4^th of the Global 2,000. It has adopted the subscription-based business model that has become the gold standard for tech today. The platform’s ability to integrate with databases like IBM IBM, Microsoft MSFT, SAP SAP and AWS AMZN, as well as other cloud analytic applications, makes this big data analyzer attractive to any firm.

This company is seeing unbelievable topline consistency that hasn’t faltered below 50% since it went public in April of 2017. The firm is toeing the line of profitability due to a significant increase in sales and marketing spending to establish their brand and develop a best-in-class reputation.

Alteryx offers both on-premise and cloud-based services, depending on customer needs. The firm is investing an increasing amount into its cloud technology to broaden its customer scope and ability.

Everything about this stock is enticing excepted for its ballooning valuation. AYX is trading at 19.6x forward P/S, which is more than 3 times the software industry average and the highest that these shares have been valued at since they went public.

AYX has been driven up by consistent quarterly earnings beats and guidance improvements. This year the company is expected to increase its sales by over 80% and turn its bottom-line deficit into profitability.

I believe that there is a good chance that investors ran this stock up a beyond its current intrinsic, but I see this as an excellent long term play, especially as AYX’s valuation falls. I would wait for a dip before jumping into a position on this overrun stock.

Splunk SPLK

This stock has been trading below its potential all year with an acquisition frenzy instilling concern in investors. I see this as a great opportunity with Splunk’s most recent deals creating synergies beyond the purchase price.

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We’re officially living during the Data Science revolution. As the driving force behind machine learning and Artificial Intelligence (AI), data science can be found at the heart of some of today’s most exciting technological innovations–from self-driving cars to surgical robots and beyond.

So it should come as no surprise that the best and most exciting careers of today and tomorrow belong to those who know how to work with large sets of data in a variety of environments. The Machine Learning & Data Science Certification Training Bundle will help you get certified in this increasingly important field, and it’s currently available for over 95% off at just $25.

With eight courses and 48 hours of in-depth content, this training will introduce you to both the fundamentals of data science along with its more advanced platforms and methodologies. 

After an introduction to the basics, you’ll learn how to implement important deep learning frameworks using Python, integrate TensorFlow into your projects, gain valuable insights from complex sets of data, use R in order to build powerful networking systems, and much more. 

There’s also instruction that teaches you about the neural networks that are shaping some of the most important AI innovations in the field. 

Get the skills and certifications you need to thrive in an AI-driven world with the Machine Learning & Data Science Certification Training Bundle for just $25–over 95% off for a limited time. 

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Marketing technology has evolved rapidly over the past decade, with one of the most exciting developments being the creation of publicly-available, cost-effective cognitive APIs by companies like Microsoft, IBM, Alphabet, Amazon and others. These APIs make it possible for businesses and organizations to tap into artificial intelligence (AI) and machine learning (ML) technology for both customer-facing solutions as well as internal operations.

According to Statistics MRC, the Machine Learning as a Service (MLaaS) market is expected to grow to 7.6 billion dollars by 2023. The impact AI/ML will have on businesses over the long-term promises to be revolutionary.

Authoring Efficiencies Powered by AI and ML

The current application of AI and ML in content management is more akin to power-assisted steering than a self-driving car. You remain in the driver’s seat, but you now have an enhanced set of tools to deploy.

These tools make it possible to automate routine operational tasks, freeing you and your team up to focus on higher-value, innovative marketing strategies, such as nurturing your audience, growing your business, and tapping your digital channels to create highly-engaged customers and brand advocates.

We’ve identified three specific areas within content marketing that are primed for the application of AI and ML technology: content tagging using classification models, faceted image search using image processing (face and object detection), and chatbot-enabled CMS workflow (natural language processing and speech-to-text).

Auto-Tag Your Digital Content

Organizations invest millions of dollars in staff, software, platforms and more, all in an effort to create valuable content. But historically they haven’t been able to fully tap into the potential of that existing content. Imagine being able to unlock that potential in the hundreds of thousands of web pages, blog posts, case studies and other digital content you have by making them easily searchable and sortable.

Off-the-shelf AI/ML tools can help you enrich your content and make it more accessible. For example, content authors usually spend a great deal of time combing through previous posts and existing taxonomy to ensure the tags they apply to a content item are accurate and in line with the current content structure. Furthermore, with the natural churn that occurs in most businesses and organizations, this experience and knowledge can often get lost as employees leave.

Businesses can train AI/ML on their unique, niche taxonomy by having it sift through thousands of your content items to gain contextual knowledge. It can then auto-tag future posts with a higher accuracy rate, allowing your authoring team to focus on creating more content. With better taxonomy, you can serve up more relevant content to your audiences when they are visiting your websites or other digital properties, further helping to enhance their user journey and customer experience.

An AI/ML investment like this will pay for itself as it continues to learn over time and become a storehouse of institutional knowledge.

Unlock the Value of Your Media Library

If you and your team works with a lot of rich media and a complex media library, then you recognize the need for image auto-tagging. Sorting through thousands of images to find the perfect one to enhance your content can be extremely cumbersome and time-consuming.

The standard image-processing capabilities in today’s AI/ML technologies make it possible to analyze an image and identify colors, objects, people and even their emotional state, gender and estimated age. The right images matched to the right content or campaign can make all the difference in catching your audiences’ attention and increasing engagement.

Let’s take an A/B test that you might want to run on a new promotion, or a personalized hero component on your home page. By nature of their purpose, these kinds of marketing automation tactics require plenty of content variations (including the involved images) to be effective. Now scale this example to cover all the promotions you might be running at any given time or all the personalized components across your site. Identifying the necessary images to serve up across them can become extremely tedious, creating a bottleneck to rolling out new campaigns.

Using an AI/ML-enabled CMS and media library makes finding the perfect images a much quicker process. You can filter images by the different criteria noted above and find what you’re looking for in no time.

Using Chatbots to Streamline Publishing from Any Device

Natural language processing (NLP) has enabled the leader in search engines, Google, to serve up contextual results based on your location and previous search history. It’s now possible to tap into this technology to enhance your CMS and increase the efficiency of your internal operations.

Any mention of voice applications likely conjures up thoughts of Alexa or Siri. However, it’s possible to embed the processing power of this functionality into your CMS, in the form of a modified chatbot, to serve as a concierge for your workflow activities.

You can then use your smartphone to publish an item, for example, simply by talking to the CMS. With mobile-friendly options, you can publish content or move it through workflow on the go, whether you’re at a conference or on a subway commuting to work.

The Value of AI/ML for Businesses

AI and ML are poised to revolutionize content marketing operations. By investing in these powerful technologies, you can expect to free up people’s valuable time, allowing them to focus on more strategic work. At some point soon, the conversation will stop revolving around whether you should use these technologies and will be about whether you were an early or late-adopter and what the resulting impact is to your business.

Since machine learning at its core is about learning, the sooner you can embed it into your internal process, the more time the technology will have to become proficient in your specific business operations. Additionally, your team will gain expertise on how best to use the tools to enhance their productivity and can use the time saved to invest in better marketing strategies, putting you at a great advantage over your competitors.

An often-overlooked side benefit is an increase in employee engagement, who will feel like they are contributing to exciting, innovative, higher-value work, rather than performing routine, repetitive daily tasks. An investment in AI/ML for content marketing operations has the potential to pay for itself many times over, essentially making adoption of the technology a win on many fronts.

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Musk is wrong to worry about artificial intelligence (AI) being a threat to humanity, so I agree with Zuckerberg. And Zuckerberg is wrong to dismiss all concerns about AI, so I agree with Musk. But neither of them are worrying about the right things.

AI is transforming almost every aspect of our lives, from the workspace to the political arena. You can’t open a newspaper today without reading a story about some impressive advance in AI.

Are machines taking over people’s jobs? Are algorithms having an impact on political debate? Will robots transform warfare? Are we sleepwalking into some dystopian future?

We’re working on ‘AI safety’

First, let’s put to rest Elon Musk’s worry. The machines aren’t about to take over the world anytime soon. Those of us working on building intelligent machines appreciate how much of a challenge remains. We’re not going to wake up anytime soon and discover the machines are in charge.

Most of my colleagues working in AI estimate it is at least 50 years before we can build machines as smart as humans. And when we do, it’s not inevitable they’ll be able to make themselves even smarter still.

So, there is plenty of time to ensure the machines are working in our best interests. And there’s a healthy community of researchers working on the topic of “AI safety” to ensure that outcome.

But that doesn’t mean we can simply put our feet up and wait for the bright future. There’s a lot to worry about. Some AI is smart, some is stupid. We’re starting to give responsibility to algorithms that aren’t actually very intelligent.

Joshua Brown discovered this to his cost in May last year. He was immortalised as the first person killed by their autonomous car. His Tesla was driving down the highway in “autopilot mode” when it hit a truck turning across the road. Mr Brown had too much faith in the technology.

Another worry is the impact AI is having on political discourse. When millions of Donald Trump’s Twitter followers are robots, you have to worry if human voices are being drowned out by computers. If the news you see on Facebook is decided by algorithms, who decides on the biases in these algorithms?

A third worry is the impact AI will have on the workforce. There’s no fundamental law of economics that requires new technologies to create more jobs than they destroy, which has been the case so far. There are more people working today than ever, and unemployment is at historically low levels.

There were 50 years of pain after the Industrial Revolution

But this time could be different. In the Industrial Revolution, machines took over much manual labour but left us with many cognitive tasks. In the AI revolution, machines will take over many of these cognitive tasks. What is left for us?

The Industrial Revolution offers us a good historical precedent for dealing with change like this. Before the industrial revolution, many people worked out in the fields. After the Industrial Revolution, machines took over many of these jobs. And new jobs were created in offices and factories.

We invented universal education so people were educated for these new jobs. We invented labour laws and unions so the owners of the production didn’t exploit their workers. We invented a welfare state and pensions so all of us shared the increased wealth. We made some deep, structural changes to society so everyone shared the benefits of increasing productivity.

These changes didn’t happen overnight. Indeed, there were 50 years or so of pain before many workers saw their quality of life lift above what is was before the Industrial Revolution.

This then is the challenge we face today — except the AI revolution will likely happen even faster than the Industrial Revolution. For this reason, we need more regulation.

Many tech companies like Facebook and Google are driven by opaque algorithms and are increasingly impacting on our lives in undesirable ways.

Facebook is now the largest news organisation on the planet, yet it doesn’t have the same responsibilities as the traditional press.

Google is starting to know too much about our lives, and will need to be broken into parts to prevent it becoming a monopoly. Actually, by creating the holding company Alphabet, Larry Page and Sergey Brin have made the regulators’ job much easier.

And it’s hard to know where to begin with Uber, one of the most badly behaved of them all.

If Google or other companies won’t pay taxes, then more countries besides Australia and the UK need to make a Google tax to force them to do so.

Silicon Valley can’t wash its hands of the responsibility that comes with immense reach.

For too long, we (and our governments) have been seduced by the promises spun by technologists.

AI is one of the few hopes for tackling many of the problems that challenge us today like climate change and the ongoing global financial crisis.

But with immense power comes responsibility.

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