How artificial intelligence can be used for potential business value.

The use of the artificial intelligence market is expected to grow to $390.9 billion by 2025, and industries within the space show a similar trend that is automotive AI, for example, is expected to grow by 35% year over year, and manufacturing AI will likely increase by $7.22 billion by 2023. However, according to top industry analysts, most (about 80%) of AI projects stall at the pilot phase or proof-of-concept phase, never reaching production. In many cases, this is due to a lack of high-quality data. Ethical and responsible AI continue to be obstacles for many companies, which often lack the resources or internal talent to build unbiased models in a time where AI is making increasingly impactful decisions. Companies also face an uphill battle with scaling and automation.

Believe in Your Data

The main factor of using artificial intelligence confidently in one’s business is to understand the value of data. People need high-quality training data to launch effective models. So, defining the data strategy upfront, including what the data pipeline will look like, will be crucial to success. Many data scientists and machine learning engineers say that about 80% of their time is spent wrangling data.

• The first step of the process is to collect data. One must start with a clear strategy for data collection. They should think about the use cases they are targeting and ensure that their datasets represent each of them. They must have a clear plan for collecting diverse datasets. Implement the data annotation process would require a diverse crowd of human annotators. The more accurate their labels are, the more precise their model’s predictions will ultimately be. Various perspectives will enable the user to cover a broader selection of use and edge cases. At the data collection and annotation phase, it’s critical to have the right plan for tooling in place. Be sure to integrate quality assurance checks into your processes as well. Given that this step takes up most of the time spent on an AI project, it’s especially helpful to work with a data partner in this area.

• The next step of the process is to train data. Feeding the ML machine with the right data is a vital step. It affects the characteristics of the machines as well as achieving accuracy in the result.

• Once the model reaches the desired accuracy levels, it is ready to launch. Post-deployment, the model will start to encounter real-world data. The user should continue to evaluate the model’s output; if it fails to output the correct data, a loop that data back through the validation phases. It’s helpful to keep a human-in-the-loop to manually check a model’s accuracy and provide corrected feedback in the case of low-confidence predictions or errors.

The Ones Who Tried and Won

In 2017 John Deere acquired Blue River Technologies, and together they’re poised to revolutionize pesticide use. Their AI models use drones and computer vision algorithms to identify weeds on farms. Doing so enables pesticides only to be sprayed on the weeds, rather than all crops in a field. Spending on pesticides was around $20 billion per year, but these efforts, it is expected to lead to a 90% reduction in pesticide costs. The methodology for this AI project is precise image segmentation. This method requires labeling data at the pixel level to determine which component of an image is weed versus crop. As one might imagine, the annotation process is very complex and involved. It requires both a comprehensive tooling interface and human levelers with a deep level of expertise in segmentation.

Use of AI in other Businesses

The manufacturing industry is using AI to automate logistics and supply chains. Nokia, for example, uses machine learning to alert an assembly operator when quality deviates. Specifically, if there are inconsistencies in the production process. AI may also monitor and track packages as part of a smart factory monitoring system, reducing lead time and preventing overstocking, or it can monitor throughput and downtime, highly impactful factors from a cost perspective. There are many automotive AI trends worth highlighting, including automation and safety, voice assistance, and personalization, among others. Self-driving cars are perhaps receiving the most fanfare, as these have the power to most dramatically change our daily lives.

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Women have carved a niche for themselves in almost every field one could possibly think of. Business, finance, marketing, IT, law – you name it and you’ll find women doing a great job without any difficulty. However, data science is that one field that’s still a little unexplored by women. And considering the potential that women have, this field can see remarkable changes in the days ahead.

What’s been observed so far is that the data science industry is men-dominated and the number of females who’ve made a career in this field is relatively less. Almost all the data scientists out there are men and this itself raises questions as to why is that so when the skills required to become a data scientist are gender-neutral. Be it critical thinking, structured approach, creativity, intuition, analytical ability, problem-solving techniques, or any similar skill for that matter – all these have nothing to do with whether the person is a male or a female. Women too stand the potential to inculcate all of this and emerge out to be extremely credible data scientists. Then, why aren’t the number of women data scientists shooting up?

The fact that data science as a career isn’t something that many people are aware of which is why the number of data scientists across the globe is very less. Making people aware of the same will not only open the door of opportunities for people all around but also bring in more women to the field.

No wonder, there’s a staggering difference between the number of men and women in the field. A number of factors have contributed to this like the workplace culture, the level of confidence, lack of interest, no early exposure and inherent bias. Now is the time that women are made to understand what are the opportunities available for them, what will those opportunities involve, and what the quality of life looks like for someone in this role. Women have the potential to cater to business needs and with adequate knowledge of the same can help them achieve results like never before. Additionally, being exposed to the required skills well in advance makes it easy for them to achieve the desired objective.

Yes, there are many qualified women out there who fit the eligibility criteria just right but the hurdles don’t seem to stop. Gender diversity could be unbalanced based on the way an organization recruits for a position. Also, the fact that most of the recruitments rely on referrals to determine the top candidates cannot be overlooked. No wonder, it has been observed that teams with more or less equal number of men and women are more likely to be creative, share knowledge and finish tasks faster and efficiently when compared to teams of unequal genders.

However, a point worth noting is that closing the gender gap in this field of data science shouldn’t only be about reaching a certain ratio of women to men within the workplace. Even though recruitment is still an issue, the focus should be more about empowering women. It is now time that we change how the system functions with fewer women followed by steps such as informing women interested in or entering this field that skill proficiency isn’t based on gender.

With that being said, need of the hour is to bring awareness about this current situation, highlight the growth opportunities available, and bring the benefits of gender diversity to light. Well, better late than never!!

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Data science is weaved into our lives in many aspects. Its range of adaptability across industries and sectors is wide. Not only in private organizations, but data science serves in the public sector as well. The government agencies and institutions are using the technology to serve their country and countrymen better. Through analyzing the trends and running millions of simulations, such agencies can explore new patterns to make more informed decisions in favor of the nation. For example, the Australian government is employing big data at both federal and state-level to have more efficient practices and save money of taxpayers. The government bodies are using predictive analytics to detect tax fraud. It also helps improve emergency services, such as Ambulance Victoria, for efficient allocation.

It was in the year 2012 when data science started taking a significant role in federal actions of the US government. The White House presented with a press release titled “Big Data is a Big Deal” on March 29, 2012, that announced the “Big Data Research and Development Initiative” under former President Barack Obama. Significantly, BDRDI was a US$ 200 million-investment which was spread across 6 agencies. The action has been taken to improve the US’s ability to extract knowledge and insights from large and complex collections of digital data. The press release also stated that it has been done “to help accelerate the pace of discovery in science and engineering, strengthen our national security, and transform teaching and learning.”

For example, the Department of Defense in the US has employed big data. To extract its usefulness, the ‘CINDER’ program has been created in order to boost national defense by leveraging new detection techniques that are capable of identifying foreign intelligence over the government and military computer networks. Additionally, to test its potential harmful ends, a program named ‘Mind’s Eye’ was developed which was aimed at developing visual intelligence in machines that is relevant for mass surveillance and advanced weaponry.

The White House document described the major goal behind the initiatives of this research as “Whereas traditional study of machine vision has made progress in recognizing a wide range of objects and their properties—the nouns in the description of a scene—Mind’s Eye seeks to add the perceptual and cognitive underpinnings needed for recognizing and reasoning about the verbs in those scenes. Together, these technologies could enable a more complete visual narrative.”

Here are some of the ways in which US government bodies are using Data science to their fullest.

•  Data science has been used by the Federal Housing Authority (FHA) to predict claim rates, default rates, and repayment rates. Also they further leverage collected data to develop cash flow models which would help them determine the amount of premium in order to maintain positive cash flow.

•  ‘The Big Data to Knowledge’ or BD2K is an initiative taken by the National Institutes of Health (NIH) to accelerate biomedical research. The program also serves to maximize community involvement and to foster the discovery of new knowledge.

•  The department of Education is using data science to develop learning analytics and data mining systems. Such systems can monitor and correct an online student’s study pattern. This analytics system can also detect boredom from patterns of key clicks in real-time.

•  One of the leading users of data science is the US Department of Homeland Security (DHS) which uses big data strategies for interoperability to integrate and compare data from various security agencies. This helps them predict or identify potential threats to the country.

•  Additionally, the National Center for Atmospheric Research integrates data and research from universities, utility companies, and interested parties using the advents of big data technology to facilitate with more accurate weather forecasts. It also helps in determining energy production and needs.

•  Moreover, NASA is also working in association with the US Forest Service to improve an integrated data strategy in an effort to predict ground conditions, the weather, and risks of forest fires.

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Retail-specific artificial intelligence startups are boasting billions in funding, cutting deals for tech innovations that range from robotics to carefully-crafted communications — and it’s only just getting started.

Driverless grocery delivery options and Amazon Go’s cashier-less stores present very visible examples of AI in the wild, but there’s far more in store — literally.

Reimagining Operations

Only 26% of today’s active AI use cases in retail encompass operational technology, but those cases pack a serious punch.

Data-driven intelligence can help retailers make smarter moves at every step, from procurement to on-shelf pricing. Optimized supply chain planning, theft detection, seamless pick-and-packing, and trend prediction can all be shouldered by clever AI algorithms and savvy robotics.

For example, the team at Bossa Nova builds real-time robots to perform basic inventory management: shelf scanning, data mapping, and product monitoring. The company’s already cut a major deal with Walmart for quick, incredibly accurate shelf stocking assistance, and are further developing their technology to meet needs beyond standard grocery and big box stores.

By saving associates’ time and guesswork for efficient stocking — all while collecting valuable data in purchasing trends — the friendly robots offer perfectly seamless integration into the modern retail space with big value benefits.

Communicating with Customers

The perks of AI for warehousing, inventory, and supply chain applications present themselves clearly, both in efficiency and sheer ROI. But the key to effectively engaging an AI strategy for retail rests in plain sight: optimizing interaction with the customer.

In an already saturated marketplace, engaging the customer through quality experiences and genuine communication can make the difference — and the sale — even above and beyond the all-powerful price point.

Subway Restaurants has already made international news this year with a targeted communication campaign, locking in customer loyalty through innovative one-to-one mobile marketing tied directly to the in-store experience. The company launched a partnership with Mobivity Holdings Corp. using Google’s Rich Communications Services to speak directly to customers — and place tailored promotions right within reach.

The custom communication has already garnered Subway over 10 times a return on their investment, taking data-driven details and AI capabilities to task in providing a premium customer connection. Loyalty — once defined by a card subscription or in-store coupon — has gotten smart and gone mobile, and it’s getting people in the door.

Personalized Purchasing

Subway’s mindful messaging illustrates just one angle of AI for personalized purchase experience: retailers are employing chatbots, voice shopping, and even custom apps to meet shoppers on their own terms.

Beauty powerhouse L’Oreal joins trendsetters like Sephora and Estee Lauder in embracing AR apps to give shoppers a real-time product testing experience: the company acquired startup Modiface in March of 2018. Modiface specializes in crafting incredibly realistic AR try-on apps, revolutionizing the customer shopping experience for hair, make-up, and much more.

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