Artificial intelligence is gaining momentum in the consumer landscape. Digital assistants, chatbots and every other kind of artificially intelligent service are taking up significant column space in the tech and business press.

Artificial intelligence is gaining momentum in the consumer landscape. Digital assistants, chatbots and every other kind of artificially intelligent service are taking up significant column space in the tech and business press. While AI has been around for years, adoption has boomed in recent times. It’s not a matter of if but when your competitors will achieve a strong advantage by introducing bots to streamline customer service, take some pressure off help desk or optimise HR processes.

According to Gartner, “Global business value derived from artificial intelligence is projected to total US$1.2 trillion in 2018, an increase of 70 per cent from 2017.”

If you are not embracing AI, your organisation could be in big trouble in the future. Even Facebook are worried, announcing in July 2018 that it would dramatically increase investment in AI research and development to ensure that it doesn’t fall behind as a technology innovator.

The opportunities to leverage AI in your organisation are many, but where to start?

Here are five ideas to consider:

  1. Give everyone a digital assistant. As CEO, you may have people to help manage your schedule, book appointments and make sure deadlines are met to enable you to spend more time on profitable activity. What if your teams had a similar facility to book meetings around difficult schedules, reserve resources, book transport and remind them of upcoming due dates? A bot can do all this and more with minimal set-up. What better way to improve the value each employee can deliver to the business, and simultaneously show people that their time is valued? Best of all, this assistant doesn’t get a pay cheque or holidays and never calls in sick.
  2. Improve collaboration and engagement with your digital workplace. After a huge investment in time and money it can be frustrating when employees do not adopt their Digital Workplace as quickly as expected. A digital workplace bot can help people find what they are looking for without searching through sections and menus, and can even suggest content. This bot can connect people with particular skills or experience, and locate relevant documentation such as previous sales proposals or contracts to assist with new projects and minimise rework.
  3. Give IT a break. We have all had a moment where we have forgotten how to complete a certain process and are either too embarrassed to ask IT (they have already told us how to do it twice) or simply don’t have time to wait for overloaded Helpdesk to get to us. Enter the Helpdesk Bot. Recognising “How do I” searches, this bot will take the user through a process step by step, freeing IT and ensuring the quickest resolution for the user.
  4. Make HR processes less painful for everyone. HR forms are generally unavoidable for booking leave, requesting equipment or submitting new ideas, not to mention time spent trawling through policies to work out what is and isn’t allowed. A bot, with access to the relevant information and systems, could assist staff to complete forms via a Q&A format, and add value by validating content and clarifying requirements, saving the HR team from a trying back-and-forth. “Book holidays for the first week in December” is easier than having to look up the dates. The bot can then ensure the required leave is available, then log the request, calendar the dates and automatically set up out of office replies.
  5. Welcome new starters. Onboarding employees is often done poorly, giving a bad first impression and wasting IT, HR and the new starter’s time. Introducing AI to the process ensures a consistent experience, conserving resources, and guiding the inductee through a series of introductory topics to provide information in manageable chunks. It can also ensure reliable completion of legal requirements such as safety briefings or acceptance of conditions. Employees who participate in a structured onboarding program are 69 per cent more likely to stay with an organisation for three years.

In the right hands, technology such as Microsoft Azure AI services can get you on the AI journey quickly. It’s worth investigating how AI can help your business… before it helps your competitors.

Source: The CEO Magazine 
In Collaboration with Huntertech Global

AI is almost everywhere in the news today, and the drive to create and implement AI solutions is creating an enormous talent gap.  An estimated 80% of companies are already investing in AI and most are facing challenges hiring the capabilities they need to implement a useful AI application or product.  It’s clear that there is an intensively competitive market for artificial intelligence and machine learning specialists.  Many companies first attempt to hire Ph.D.-level data scientists with expertise in AI algorithms and “feature engineering.” Some analysts have even equated “AI talent” with such researchers.

However, AI talent goes far beyond machine learning Ph.D’s.  Equally important and less understood are the set of talent issues emerging around AI product development and engineering. Most firms have not filled these roles, and their AI projects are suffering as a result.

The AI Engineer Role

Because some others have already realized their importance, let’s focus first on engineering skills. A very useful article recently pointed out the difference between machine learning researchers and machine learning engineers.  The key takeaway is that most companies need engineers to help develop products and production applications, rather than a researcher to help push the boundaries of AI technique and technology.

These engineering skills include creating technology architectures that scale, writing and deploying bulletproof software, and integrating AI capabilities with existing systems. The people in AI engineering roles need to know something about AI, but just as much about programming, computing, and corporate IT environments. Such skills are becoming increasingly important over time as AI knowledge and tools mature, and as algorithms and techniques become commoditized.

The AI Data Czar Role

AI initiatives also need data experts. We’ve also argued elsewhere that the machine learning race is increasingly a data race in which unique data, rather than cutting-edge modeling, is what creates a valuable AI solution.  Unfortunately, sourcing and managing data is a skill set that does not often overlap with algorithm development. The AI data czar is typically a position that is created over time through experience, rather than hired out of school, although education in computer science or statistics can be very helpful. The role encompasses such capabilities as:

  • Knowing what data sources are useful to address an AI question or problem;
  • Being aware of how data is used in algorithms;
  • Assessing data quality;
  • Cleaning and treating data;
  • Having a focus on detail (and being a stickler for data quality);
  • Possessing the strength to push back at technical teams;
  • Knowing the typical ways to transform data.

Data management also requires business knowledge.  Let’s discuss a simple example.  Our startup uses machine learning to bring automation to strategy consulting, and one of the key inputs we use is the financial information included in annual reports.  This data is inherently full of gaps.  Not every company reports the same set of metrics, and the reason for failing to report is most often that there is nothing to report in the category.  For example, many companies do not bother reporting their research and development spending because they have none!  This means that the best course of action for filling most of these gaps (which must be filled for the algorithms to work their magic) is to fill them with zeros, representing no spending.

However, in the world of data science and machine learning, zero-filling data is extremely uncommon and filling with the median value is a generally accepted best practice.  Our application is the rare case where median filling actually introduces errors into the data set—for example by assigning an average amount of research and development investment to every company, when 70% of the market actually spends nothing on R&D.  If we had handed off data management to the tech team, as many companies do, we would have headed down the wrong path.  Instead, by having an informed business team deeply involved in the AI development process, we are able to catch potential problems.

The Business Leader and AI Translator Roles

AI groups also need a role at the intersection of business strategy and AI methods. Such a person, usually a somewhat senior executive, is able to translate strategic objectives and business models into the types of AI that can advance them. Unfortunately, the role of a business leader with some understanding of AI techniques is rarely discussed, and even less often filled.


The result is that AI is often used to create either off target or sunk cost projectswhere the technology investment does not yield the ROI anticipated by the board or the leadership team.  Our experience working with boards and leaders is that creating a solid AI product that provides either customer, employee, operational or investor value is about 40% problem and product definition, 40% data sourcing, cleaning, filling, and merging, and only 20% algorithm development.

Solving these problems requires ongoing partnership between business and technology.  Yet most companies do not have a clear point of view about how AI can help organizations make better, more informed and faster decisions, or smarter products and services.  Automating parts of decision-making and product development requires a person that can work at the intersection of strategy, business models, code development, algorithm creation, and product development—a rare breed.

Having someone in this role even pays dividends when it comes to algorithm development.  From rules-based systems to logistic regression to neural networks and beyond, the algorithms that are used in AI each have different characteristics, good and bad.  Although we wouldn’t expect all business leaders to know these, we would expect a good AI business leader/translator to engage with developers on these pros and cons to help drive the right decision.  For example, neutral networks, though powerful, lack explainability.  It is difficult to say exactly why the model returns the results that it does.  For many products, a “black box” solution won’t do—the users want or need to understand how it works.

Many companies rush into the AI race without clear objectives, hope a brilliant AI researcher and a technology team can create something great without guidance, and end up with little to show for it.  Recruiting an AI quarterback to provide the business input, and ensuring success with well-defined metrics, is the most important job that most companies miss entirely. Some have argued for the importance of the translator function for traditional business analytics, but given the complexity of AI it is even more important with that set of technologies. Indeed, many large AI groups will need multiple people to play the translator role.

The businessperson who fills this role does not need to become a programmer, know the best AI tools from vendors, or delve into the nuances of neural networks versus logistic regression.  He or she does, however, need to understand the basics of how different types of AI work and the data sets that will be deployed with them., Such individuals should also have a desire to get deeply involved and work iteratively with the AI team rather than throwing requirements “over the wall,” leaving the machine learning team with the tough decisions.  In addition, they need to create a clear economic use case and product road map that produces value for customers, employees, partners or investors. In most cases, these individuals should lead the AI group, and the researchers, engineers, and data czar should report to them.

Having someone on board that who is in or reports directly to the C-suite with an understanding of these topics, and who can oversee the other important AI roles we have discussed, will help the organization achieve its core objective—value for stakeholders—while avoiding the costly, unproductive cycles we often see in poorly managed AI development.

Source: Harvard Business Review

Landr, a website that uses AI to master songs, can now master music in multiple sonic styles. The new options allow musicians who use Landr’s AI to have their songs polished in a variety of different sonic styles. Before, it was a one-size-fits-all approach.

Subscribers to Landr can now pick between three different types of mastering sounds. “Warm” is described as having vintage warmth and being thick and smooth; “Balanced” has a focus on balance, clarity, and depth; and “Open” is more modern, with attention to making the audio more punchy and present.

Offering different styles should make the service appeal to a wider variety of musicians, as different genres require different mastering approaches. What might work for a bass-heavy dance song, for example, might be completely wrong for an acoustic singer-songwriter track. Also, outside of genres, musicians simply might have personal preferences about whether they like a brighter sound or something fatter and bass-heavy.

In addition, Landr now has volume matching for playback, which lets you A/B between your original upload and the mastered version at the same volume. Comparing at the same volume is important for staying neutral about what you think sounds good, and it can help you avoid falling into the “louder is better” trap. Lastly, there’s now a “Mastering Preferences” panel so you can save your favorite settings for future sessions and also apply them to bulk uploads.

Landr’s new Mastering Preferences.
 Image: Landr

Mastering is the final step in audio post-production, and it uses tools like EQ and compression to make audio sound polished and optimized to play on any platform. While humans have traditionally handled this role, AI mastering services have become increasingly popular over the past few years, and Landr is one of the more recognizable names.

Landr costs a fraction of what you would pay a human mastering engineer, but the caveat is that it’s more of a catch-all approach. You can’t give feedback to Landr’s AI the way you can with a human engineer, and, up until now, there was only one option for how Landr’s mastering sounded.

The new mastering styles will be available to all Landr subscribers for a limited time. Eventually, the new options will be limited to Landr’s Advanced and Pro plans, along with the Mastering Preferences panel.

Source: The Verge

The latest AI algorithms are probing the evolution of galaxies, calculating quantum wave functions, discovering new chemical compounds and more. Is there anything that scientists do that can’t be automated?

o human, or team of humans, could possibly keep up with the avalanche of information produced by many of today’s physics and astronomy experiments. Some of them record terabytes of data every day — and the torrent is only increasing. The Square Kilometer Array, a radio telescope slated to switch on in the mid-2020s, will generate about as much data traffic each year as the entire internet.

The deluge has many scientists turning to artificial intelligence for help. With minimal human input, AI systems such as artificial neural networks — computer-simulated networks of neurons that mimic the function of brains — can plow through mountains of data, highlighting anomalies and detecting patterns that humans could never have spotted.

Of course, the use of computers to aid in scientific research goes back about 75 years, and the method of manually poring over data in search of meaningful patterns originated millennia earlier. But some scientists are arguing that the latest techniques in machine learning and AI represent a fundamentally new way of doing science. One such approach, known as generative modeling, can help identify the most plausible theory among competing explanations for observational data, based solely on the data, and, importantly, without any preprogrammed knowledge of what physical processes might be at work in the system under study. Proponents of generative modeling see it as novel enough to be considered a potential “third way” of learning about the universe.

Traditionally, we’ve learned about nature through observation. Think of Johannes Kepler poring over Tycho Brahe’s tables of planetary positions and trying to discern the underlying pattern. (He eventually deduced that planets move in elliptical orbits.) Science has also advanced through simulation. An astronomer might model the movement of the Milky Way and its neighboring galaxy, Andromeda, and predict that they’ll collide in a few billion years. Both observation and simulation help scientists generate hypotheses that can then be tested with further observations. Generative modeling differs from both of these approaches.

“It’s basically a third approach, between observation and simulation,” says Kevin Schawinski, an astrophysicist and one of generative modeling’s most enthusiastic proponents, who worked until recently at the Swiss Federal Institute of Technology in Zurich (ETH Zurich). “It’s a different way to attack a problem.”

Some scientists see generative modeling and other new techniques simply as power tools for doing traditional science. But most agree that AI is having an enormous impact, and that its role in science will only grow. Brian Nord, an astrophysicist at Fermi National Accelerator Laboratory who uses artificial neural networks to study the cosmos, is among those who fear there’s nothing a human scientist does that will be impossible to automate. “It’s a bit of a chilling thought,” he said.

Discovery by Generation

Ever since graduate school, Schawinski has been making a name for himself in data-driven science. While working on his doctorate, he faced the task of classifying thousands of galaxies based on their appearance. Because no readily available software existed for the job, he decided to crowdsource it — and so the Galaxy Zoo citizen science project was born. Beginning in 2007, ordinary computer users helped astronomers by logging their best guesses as to which galaxy belonged in which category, with majority rule typically leading to correct classifications. The project was a success, but, as Schawinski notes, AI has made it obsolete: “Today, a talented scientist with a background in machine learning and access to cloud computing could do the whole thing in an afternoon.”

Schawinski turned to the powerful new tool of generative modeling in 2016. Essentially, generative modeling asks how likely it is, given condition X, that you’ll observe outcome Y. The approach has proved incredibly potent and versatile. As an example, suppose you feed a generative model a set of images of human faces, with each face labeled with the person’s age. As the computer program combs through these “training data,” it begins to draw a connection between older faces and an increased likelihood of wrinkles. Eventually it can “age” any face that it’s given — that is, it can predict what physical changes a given face of any age is likely to undergo.

 PHOTO: Grid of AI generated human faces.

None of these faces is real. The faces in the top row (A) and left-hand column (B) were constructed by a generative adversarial network (GAN) using building-block elements of real faces. The GAN then combined basic features of the faces in A, including their gender, age and face shape, with finer features of faces in B, such as hair color and eye color, to create all the faces in the rest of the grid.



How AI Is Transforming The Health Sectors In 2019 Artificial intelligence (AI), sometimes called machine intelligence, is an intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. It emphasizes the creation of intelligent machines that act and react like humans. AI-powered technologies are getting to be more pervasive […]

Artificial intelligence (AI), sometimes called machine intelligence, is an intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. It emphasizes the creation of intelligent machines that act and react like humans. 

AI-powered technologies are getting to be more pervasive across several industries in the world today; finance, agriculture, auto transport, energy, and healthcare. The use of this technology is growing so remarkably that the market is expected to reach a whopping 34 billion US Dollars by 2025

Artificial intelligence (AI) in healthcare is the use of algorithms and software to approximate human cognition in the analysis of complex medical data. The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes.

AI has the capability of detecting meaningful relationships in a data set and has been widely used in many clinical situations to diagnose, treat, and predict medical outcomes. 

What distinguishes AI technology from traditional techniques in health care is the ability to gain information, process it and give a well-defined output to the end-user. AI does this through machine learning algorithms, which can recognize patterns in behavior and create its own magic. 

AI-powered solutions will not only enable healthcare workers to do more with less, but it will also support operational initiatives to increases cost saving, improve patient satisfaction, and satisfy healthcare staffing and workforce needs. While we may just be at the early stage of this transformation, here are some major ways AI is already transforming the health sectors in 2019.

Managing Medical Records And Other Data:

One of the most crucial aspects of every business sectors, finance, spanning education, auto transport, agriculture, fashion, etc., is compiling and analyzing information. The case is the same for the healthcare sector (medical records, health status, a treatment designed, and other records). Hence, data management is the most paramount and widely used application of artificial intelligence, AI.

Robots collect, store, re-format, and trace data to provide faster, more consistent access. 

Reducing Diagnostic Errors:

One difficult the healthcare sector faces is the inability of physicians and healthcare regulators to attain perfection. As a matter of fact, research shows that a whopping 10 perfect of patient deaths and a number of hospital complications are the result of diagnostic errors. 

Howbeit that, AI systems help reduce these errors substantially. A deep learning algorithm couls more accurately determine specific threats present in a large percentage of cells, tissues, and organs it analyzes. 

A study reveals that combining human effort with automated tools can raise the diagnostic rate to 97 percent or higher. AI systems are designed to empower doctors to provide a higher level of care. These AI systems can ensure compliance in every step of the process.

Virtual Doctors And Medical Apps:

Gone are the days where the normal procedure for a sick person to receive treatment is: Go to the hospital – Pay for cards – Wait to see the consultant – then you can see the doctor. Today, through the help of AI technology, there are online apps that give basic health information and advice to the sick. 

With this app, you don’t even have to wait till you fall sick, once you begin to notice any strange body symptoms, you can log in to this app (if you have a membership account) and you’ll get the medical advice you need, right in your comfort zone.

Also, AI technologies provide systems, that serves as virtual doctors, to analyze data – notes and reports from a patient’s file, external research, and clinical expertise, to help select the correct individual customized treatment path. Users report their symptoms to the system, which uses speech recognition to compare against a database of illness, the system then offers a recommended action. 

Health Monitoring:

Wearable health trackers and gadgets, like those from Fitbit, Apple, and others, helps monitor the health rate and activity levels of users.

Just as some reliable auto transport services use sensor devices to alert their truck drives of different issues, these health monitoring devices would send alert to the user to get more exercise and can share this information with doctors (and AI systems) for additional data points on the needs and habits of patients. 

AI-assisted Surgery:

With an estimated value of $40 billion to healthcare, robots can analyze data from pre-operation medical records to guide a surgeon’s instrument during a surgical operation.

AI-assisted surgery is often described as “minimally invasive surgery,” so it causes less harm to patients.  Via artificial intelligence, robots can use data from past operations to inform new surgical techniques, thereby reducing the stress for surgeons and a 21 percent reduction in a patient’s hospital stay.

By Line:

The IT industry is constantly changing. There will be a stream of new healthcare perations empowering a culture of entrepreneurship, creativeness, and innovation. Are you ready?

Source: Thrive Global

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