Artificial intelligence (AI) is rapidly becoming integral to how organizations are run. This should not be a surprise; when analyzing sales calls and market trends, for example, the judgments of computational algorithms can be considered superior to those of humans. As a result, AI techniques are increasingly used to make decisions. Organizations are employing algorithms to allocate valuable resources, design work schedules, analyze employee performance, and even decide whether employees can stay on the job.

This creates a new set of problems even as it solves old ones. As algorithmic decision-making’s role in calculating the distribution of limited resources increases, and as humans become more dependent on and vulnerable to the decisions of AI, anxieties about fairness are rising. How unbiased can an automated decision-making process with humans as the recipients really be?

To address this issue, computer scientists and engineers are focusing primarily on how to govern the use of data provided to help the algorithm learn (that is, data mining) and how to use guiding principles and techniques that can promote interpretable AI: systems that allow us to understand how the results emerged. Both approaches rely, for the most part, on the development of computational methods that factor in certain features believed to be related to fairness.

At the heart of the problem is the fact that algorithms calculate optimal models from the data they’re given — meaning they can end up replicating the problems they’re meant to correct. A 2014 effort to remove human bias in recruitment at Amazon, for example, rated candidates in gender-biased ways; the historical job performance data it was given showed that the tech industry was dominated by men, so it assessed hiring men to be a good bet. The Correctional Offender Management Profiling for Alternative Sanctions, an AI-run program, offered biased predictions for recidivism that wrongly forecast that Black defendants (incorrectly judged to be at higher risk of recidivism) would reoffend at a much greater rate than white defendants (incorrectly flagged as low-risk).

Organizations and governments have tried to establish guidelines to help AI developers refine technical aspects so that algorithmic decisions will be more interpretable — allowing humans to understand clearly how decisions were reached — and thus fairer. For example, Microsoft has launched programs that identify high-level principles such as fairness, transparency, accountability, and ethicality to guide computer scientists and engineers in their coding efforts. Similar efforts are underway on the government level, as demonstrated by the European Union’s Ethics Guidelines for Trustworthy AI and Singapore’s Model AI Governance Framework.

But neither the efforts of computer scientists to factor in technological features nor the efforts of companies and governments to develop principle-based guidelines quite solves the issue of trust. To do that, designers need to account for the information needs and expectations of the people facing the results of the models’ outputs. This is important ethically and also practically: An abundance of research in management shows that the fairer decisions are perceived to be, the more that employees accept them, cooperate with others, are satisfied with their jobs, and perform better. Fairness matters greatly to organizational functioning, and there’s no reason to think that will change when AI becomes the decision maker.

So, how can businesses that want to implement AI persuade users that they’re not compromising on fairness? Put simply, they need to stop thinking about fairness — a complicated concept — as something they can address with the right automated processes and start thinking about an interdisciplinary approach in which computer and social sciences work together. Fairness is a social construct that humans use to coordinate their interactions and subsequent contributions to the collective good, and it is subjective. An AI decision maker should be evaluated on how well it helps people connect and cooperate; people will consider not only its technical aspects but also the social forces operating around it. An interdisciplinary approach allows for identifying three types of solutions that are usually not discussed in the context of AI as a fair decision maker.

Solution 1: Treat AI fairness as a cooperative act.

Algorithms aim to reduce error rates as much as possible in order to reveal the optimal solution. But while that process can be shaped by formal criteria of fairness, algorithms leave the perceptual nature of fairness out of the equation and do not cover aspects such as whether people feel they have been treated with dignity and respect and have been taken care of — important justice concerns. Indeed, algorithms are largely designed to create optimal prediction models that factor in technical features to enhance formal fairness criteria, such as interpretability and transparency, despite the fact that those features do not necessarily meet the expectations and needs of the human end user. As a result, and as the Amazon example shows, algorithms may predict outcomes that society perceives as unfair.

There’s a simple way to address this problem: The model produced by AI should be evaluated by a human devil’s advocate. Although people are much less rational than machines and are to some extent blind to their own inappropriate behaviors, research shows that they are less likely to be biased when evaluating the behaviors and decisions of others. In view of this insight, the strategy for achieving AI fairness must involve a cooperative act between AI and humans. Both parties can bring their best abilities to the table to create an optimal prediction model adjusted for social norms.

Recommendation: Organizations need to invest significantly in the ethical development of their managers. Being a devil’s advocate for algorithmic decision makers requires managers to develop their common sense and intuitive feel for what is right and wrong.

Solution 2: Regard AI fairness as a negotiation between utility and humanity.

Algorithmic judgment is demonstrated to be more accurate and predictive than human judgment in a range of specific tasks, including the allocation of jobs and rewards on the basis of performance evaluations. It makes sense that in the search for a better-functioning business, algorithms are increasingly preferred over humans for those tasks. From a statistical point of view, that preference may appear valid. However, managing workflow and resource allocation in (almost) perfectly rational and consistent ways is not necessarily the same as building a humane company or society.

No matter how you may try to optimize their workdays, humans don’t work in steady, predictable ways. We have good and bad days, afternoon slumps, and bursts of productivity — all of which presents a challenge for the automated organization of the future. Indeed, if we want to use AI in ways that promote a humane work setting, we have to accept the proposition that we should not optimize the search for utility to the detriment of values such as tolerance for failure, which allows people to learn and improve — leadership abilities considered necessary to making our organizations and society humane. The optimal prediction model of fairness should be designed with a negotiation mindset that strives for an acceptable compromise between utility and humane values.

Recommendation: Leaders need to be clear about what values the company wants to pursue and what moral norms they would like to see at work. They must therefore be clear about how they want to do business and why. Answering those questions will make evident the kind of organization they would like to see in action.

Solution 3: Remember that AI fairness involves perceptions of responsibility.

Fairness is an important concern in most (if not all) of our professional interactions and therefore constitutes an important responsibility for decision makers. So far, organizations and governments — because of their adherence to matrix structures — have tackled the question of fair AI decision-making by developing checklists of qualities to guide the development of algorithms. The goal is to build AIs whose outputs match a certain definition of what’s fair.

That’s only half of the equation, however: AI’s fairness as a decision maker really depends on the choices made by the organization adopting it, which is responsible for the outcomes its algorithms generate. The perceived fairness of the AI will be judged through the lens of the organization employing it, not just by the technical qualities of the algorithms.

Recommendation:An organization’s data scientists need to know and agree with the values and moral norms leadership has established. At most organizations, a gap exists between what data scientists are building and the values and business outcomes organizational leaders want to achieve. The two groups need to work together to understand what values cannot be sacrificed in the use of algorithms. For example, if the inclusiveness of minority groups, which are usually poorly represented in available data, is important to the company, then algorithms need to be developed that include that value as an important filter and ensure that outliers, not just commonalities, are learned from.

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WOODS HOLE, Mass. – A picture is worth a thousand words -but only when it’s clear what it depicts. And therein lies the rub in making images or videos of microscopic life. While modern microscopes can generate huge amounts of image data from living tissues or cells within a few seconds, extracting meaningful biological information from that data can take hours or even weeks of laborious analysis.

To loosen this major bottleneck, a team led by MBL Fellow Hari Shroff has devised deep-learning and other computational approaches that dramatically reduce image-analysis time by orders of magnitude — in some cases, matching the speed of data acquisition itself. They report their results this week in Nature Biotechnology.

“It’s like drinking from a firehose without being able to digest what you’re drinking,” says Shroff of the common problem of having too much imaging data and not enough post-processing power. The team’s improvements, which stem from an ongoing collaboration at the Marine Biological Laboratory (MBL), speed up image analysis in three major ways.

First, imaging data off the microscope is typically corrupted by blurring. To lessen the blur, an iterative “deconvolution” process is used. The computer goes back and forth between the blurred image and an estimate of the actual object, until it reaches convergence on a best estimate of the real thing.

By tinkering with the classic algorithm for deconvolution, Shroff and co-authors accelerated deconvolution by more than 10-fold. Their improved algorithm is widely applicable “to almost any fluorescence microscope,” Shroff says. “It’s a strict win, we think. We’ve released the code and other groups are already using it.”

Next, they addressed the problem of 3D registration: aligning and fusing multiple images of an object taken from different angles. “It turns out that it takes much longer to register large datasets, like for light-sheet microscopy, than it does to deconvolve them,” Shroff says. They found several ways to accelerate 3D registration, including moving it to the computer’s graphics processing unit (GPU). This gave them a 10- to more than 100-fold improvement in processing speed over using the computer’s central processing unit (CPU).

“Our improvements in registration and deconvolution mean that for datasets that fit onto a graphics card, image analysis can in principle keep up with the speed of acquisition,” Shroff says. “For bigger datasets, we found a way to efficiently carve them up into chunks, pass each chunk to the GPU, do the registration and deconvolution, and then stitch those pieces back together. That’s very important if you want to image large pieces of tissue, for example, from a marine animal, or if you are clearing an organ to make it transparent to put on the microscope. Some forms of large microscopy are really enabled and sped up by these two advances.”

Lastly, the team used deep learning to accelerate “complex deconvolution” – intractable datasets in which the blur varies significantly in different parts of the image. They trained the computer to recognize the relationship between badly blurred data (the input) and a cleaned, deconvolved image (the output). Then they gave it blurred data it hadn’t seen before. “It worked really well; the trained neural network could produce deconvolved results really fast,” Shroff says. “That’s where we got thousands-fold improvements in deconvolution speed.”

While the deep learning algorithms worked surprisingly well, “it’s with the caveat that they are brittle,” Shroff says. “Meaning, once you’ve trained the neural network to recognize a type of image, say a cell with mitochondria, it will deconvolve those images very well. But if you give it an image that is a bit different, say the cell’s plasma membrane, it produces artifacts. It’s easy to fool the neural network.” An active area of research is creating neural networks that work in a more generalized way.

“Deep learning augments what is possible,” Shroff says. “It’s a good tool for analyzing datasets that would be difficult any other way.”

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Human intelligence is the quality of brain that learns, extracts knowledge, acquires abstract concepts from its surrounding, whereas artificial intelligence is the ability of a machine to mimic the same tasks learning from data it receives. Intelligence is a quality that belongs to humans and if machines could play the game right, our lives would become much easier.

Timo Elliott, Innovation Evangelist, SAP said, “The rise of artificial intelligence is raising the premium on tasks that only humans can do: it is freeing workers from drudgery and allowing them to spend time on more strategic and valuable business activities. Instead of forcing people to spend time and effort on tasks that we find hard but computers find easy, we will be rewarded for doing what humans do best — and artificial intelligence will help make us all more human.”

However, despite significant advancements, AI still could not match up to human intelligence in most aspects. In the growing debate about AI vs. human intelligence, the given wisdom has been that artificial intelligence will augment human tasks, but not replace them, anytime soon. Andrew McAfee, a professor at Massachusetts Institute of Technology, noted that 20 years have passed since a computer beat world chess champion Garry Kasparov yet the gap between computer ability and human ability has only gotten more significant. He said, “We still underestimate how big, how fast, technological progress is. I still keep getting it wrong.”

In just the past two years, McAfee said, AI has defied expectations.

“Certainly AI is proving to be an invaluable tool, and intelligent workflow is going to be the labor-saving norm within just a few years,” said Scott Robinson, a SharePoint and business intelligence expert based in Louisville, Ky. “But business processes involve intelligent thought and intelligent behavior. AI is great at replicating intelligent behavior, but intelligent thought is another matter. We don’t fully understand how intelligent human thoughts develop, so we’re not going to build machines that can have them anytime soon.”

“[McAfee’s] discussion misses the fact that human workers bring deep knowledge to business processes that AI can’t capture,” Robinson continued. “An office worker knows how other human beings think and behave, so she can anticipate delays or opportunities. There are implicit tasks in all areas of business that are undocumented but natural and deeply ingrained. AI can’t get anywhere near those implicit tasks and passive knowledge.”

Moreover, a book ‘The Globotics Upheaval’ by Richard Baldwin suggests that AI will disrupt lives more than globalization, industrialization, and automation did. While he believes that the changes are inevitable, there are adaptive strategies that can be used, employing the skills that no machine can copy; creativity and independent thought.

Analyzing the unbridgeable gap between human intelligence and artificial intelligence in the near future, the right perspective would be to the complementary attributes of AI with human intelligence. The scientific researches should be focused on developing artificial intelligence applications that could integrate with human intelligence in an effort to enhance productivity within the broad restrictions of privacy and sensibilities. If we will create a collaborative world for survival for both, then it would surely open up new opportunities for many in numerous different fields.

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By analyzing the past few years you can find that machine learning has seen unprecedented improvement in businesses in easing their chores. Machine learning is a broad subset of artificial intelligence and its only chore is to train the machine to learn. If you’re looking for machine learning providers, there are plenty. Keeping in mind reliability, quality and ability best machine learning software companies are listed.

Machine learning services

Services providing machine learning are sprouting out as the demand for business solutions increases. These services work on advanced machine learning solutions that assist organizations in developing innovative business models, in data-driven decision making and solving major business challenges.

The services also develop ML-powered for the future by employing mathematical optimization, nature-inspiring algorithms, computational intelligence and pattern recognition. They follow a series of strategies and offer the best machine learning offerings for their clients.

Software companies providing ML language

Machine learning vendors also offer custom solutions for customers for managing massive data volumes. They also run cutting-edge algorithms for the performance of a task by themselves. The ML-powered applications offer the best productivity, decision making, quick anomaly detection as well as process automation. Here are a few attributes of the service providers.

Deep learning: These services offer deep learning solutions for clients to acclimate to unpredictable business situations and to discover new processes and ideas. The services assist in reducing the operational cost and in processing massive volumes of data for best actions.

Predictive analysis: These services offer cutting-edge and scalable solutions for gaining better insights into markets and customers, present data, profitable decisions, and best business results.

Natural language processing: The usage of natural language processing with linguistics, machine learning and artificial intelligence are the best services by these providers. The providers enable to assimilate NLP capabilities in bots, applications and IoT devices. By this way there is evading of complexity and documentation process is quick. With NLP expertise, there are chances for the development of next-generation digital assistant by the businesses. These assistants would be contextually appropriate as they understand the language of the customers and make appropriate decisions.

AI models: The providers develop sophisticated AI applications to satisfy business requirements and also broaden the scope of ROI by developing further business operations. By this way, business operations are also made more intelligent. The providers make sure to incorporate AI models with present business models. By assimilating AI, the providers make sure to offer a number of services that offer effective, unique and innovative solutions for customers.

Conclusion

The service providers follow a certain strategy in developing ML models. They understand the data by collecting from the right sources and assess them for a good understanding of the business issue. Then they move on to data preparation where they process data for elevate quality. They then develop train models and check its efficiency in model building mode. Finally, they evaluate and deploy.

The machine learning providers are sure to assist the business in improving operational efficiency, gain competitive advantage, and drive drastic revenue. The best machine learning software companies utilise deep learning and identify number of images and data, they overcome issues, understand spoken language and work in an effective manner.

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