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4 Ways to Successfully Scale AI and Machine Learning for Businesses

Gartner predicts that over the next twelve months: “80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization.” In the past, many organizations have failed as they rush to embark on AI and ML projects.

The problem here is that most businesses that dip their toes in the halcyon waters of AI and ML struggle to operationalize the models that they create into real-life business processes.

To calculate this, you’ll need to plan how the output of the ML model — classification, prediction, detection, recommendation, or segmentation — will be used and by whom.

It would be pointless to build an ML system that investigates consumer behavior patterns if the information gained is inaccessible, unusable, or not a planned part of the companies overarching business strategy.

Although it’s apparent that there is a shortage in data science talent on the job market, and hiring for this type of role can be challenging, AI and ML success requires much more than the skills of a data scientist.

If you’re serious about scaling and reaping the benefits that AI and ML have to offer, you should be looking to work with ML architects, data engineers, and operations managers.

From day one, it’s crucial that you identify the use case with absolute clarity, benchmark current performance, define measurable goals, and find realistic KPI’s that will determine success criteria.

For example, if you are creating and training a machine learning model to predict customer behavior, you’ll need substantial amounts of customer data, and AI systems that can employ algorithms to break down this data and turn it into actionable insight.

This approach allows businesses to leverage the tech and data that they have on their premises while utilizing the elasticity and agility that public cloud services offer.

Tech industry watchdog challenges AI-driven recruiting practices

A number of start-up companies have emerged in recent years offering employers the ability to use artificial intelligence (AI) to screen job candidates and determine their employability.

EPIC’s complaint challenges the AI-driven recruiting solutions developed and sold by a company called HireVue, which currently has more than 700 corporate customers that use its technology as part of their hiring process.

Although HireVue denies using facial recognition technology, it does acknowledge that, through online interviews with potential job candidates, its technology collects tens of thousands of data points from each video interview.

EIPC argues in its complaint that the term “facial recognition technology” is not limited to technology used for identification or authentication purposes but also includes technologies that detect and analyze basic human facial geometry and facial biometrics to predict demographic characteristics, expression, or emotions.

EPIC alleges that HireVue’s technology and algorithms are not sufficiently tested for reliability, validity, or accuracy, and points to numerous studies that conclude that AI tools often contain implicit gender and racial biases.

EPIC also points out that it is unclear how this technology, which evaluates factors such as a candidate’s eye contact, tone of voice, rate of speech, and body language, would appropriately evaluate individuals with disabilities impacting these data points, such as individuals with autism or Parkinson’s.

EPIC contends that HireVue’s “intrusive collection and secret analysis of biometric data…causes substantial privacy harms to job candidates.” Although not specifically addressed in EPIC’s complaint, there has also been growing concern regarding how and for how long personal data is stored, to whom it may be sold, and the potential for data breaches, all of which further compounds the privacy concerns surrounding the use of AI in any context.

Artificial Intelligence in India – Opportunities, Risks, and Future Potential

Given the Indian government’s recent focus on developing a plan for artificial intelligence, we decided to apply our strengths (deep analysis of AI applications and implications) to determine (a) the state of AI innovation in India, and (b) strategic insights to help India survive and thrive in a global market with the help of AI initiatives.

According to Komal Sharma Talwar, Co-founder XLPAT Labs and member of India’s AI Task Force: “I think the government has realized that we need to have a formal policy in place so that there’s a mission statement from them as to how AI should evolve in the country so it’s beneficial at large for the country.” Indeed it’s comments like Komal’s that made us realize that we should aid in determining a strategic direction for artificial intelligence development in India –

In our research and interviews, we saw consensus (from executives, non-profits, and researchers alike) that healthcare and agriculture would be among the most important sectors of focus in order to improve living conditions for India’s citizens.

is currently engaged in the following public sector initiatives: “The current areas of focus for AI applications in India are majorly focused in 3 areas: With the government’s growing interest around AI applications in India, Deepak Garg the Director at NVIDIA-Bennett Center of Research in Artificial Intelligence (and Director believes that there has been a significant growth in interest levels around AI across all industry sectors in India.

He explains that although AI attention is considerably smaller in India than in China or the USA, the increased AI interest has manifested itself in the following three ways: “1) Industries have started working to skill their manpower to enable themselves to compete with other global players 2) Educational institutions have started working on their curricula to include courses on machine learning and other relevant areas 3) Individuals and professionals have started acquiring these skills and are comfortable investing in upgrading their own skills.” Despite the initial enthusiasm for AI, there were also a few opinions from experts about a sense of unfulfilled potential and that the country could be doing far more to adopt and integrate AI technologies.

number of our interviewees mentioned the prevalence of copy-catting business models in India (taking a famous or successful business model in the USA or Europe and reconstructing it in India), as opposed to the invention of entirely new business models.

roughly 18% of the Indian GDP) have a significant potential opportunity to cater to the coming demand for data cleaning and human-augmented AI training (data labeling, search engine training, content moderation, etc).

Historically, the slower adoption of IT services by domestic Indian companies (in some cases by even by a period of around 10 years) as compared to global competitors was an indicator of the unfulfilled potential according to some experts we spoke to.

“The Indian foundation of IT services and business process outsourcing makes me believe that such AI training jobs will be even more lucrative for India than elsewhere in the future.” During the interview with him, Aakrit explained his stance with an example about the possibility that Indian BPO services providers could potentially be attractive in terms of skills and cost for tasks (which he believes will for a long time remain a manual effort) like cleaning and tagging of data in the near future.

We believe India has a major advantage over other countries in terms of talent, a vibrant startup ecosystem, strong IT services and an offshoring industry to harness the power of AI.” Kiran Rama, the Director of Data Sciences at the VMware Center of Excellence (CoE) in Bangalore also seems to agree that the cost-competitive talent in India will be an opportunity for companies looking to open offices in India: “There seems to be a lot of opportunity for companies that are setting u shop in India.

I also think there Indians are starting to contribute to the advancement of machine learning libraries and algorithms.” Subramanian Mani, who heads the analytics wing at, an online Indian grocery e-commerce firm, reiterates the idea that the IT services background in India is an advantage.

He believes that the major difference between the software and AI waves is that although India was slow to adopt software service as compared to America, this time around with the AI wave, adoption will be much faster and only slightly behind the leading countries.

Folks in India realized that they’ve been able to scale software and I think AI / ML is an extension of software development.” While software was often taught through books and in classrooms exclusively, many of the latest artificial intelligence approaches are available to learn online –

Going in, we knew that one of the key advantages for India would, in fact, be the very IT and ITeS sectors which will make it easy for Indian tech providers to transition into AI services, given that well-developed ecosystems have evolved over the past 25 years in cities like Bangalore and Hyderabad.

The state government is interested in planning and grooming for startups in this space as witnessed by the launch of the Center for Excellence (CoE) in AI setup by the GOI and NASSCOM in Bangalore.” While the advantage from the existing Indian IT sector may have been more intuitive, Madhusudan Shekar, Principal Technology Evangelist at Amazon AWS explains through an example how India’s diversity and scale (generally considered a challenge) can be an opportunity to make the best out of a tough situation: “In India, people speak over 40+ formal languages in about 800+ dialects.

To further explain,  According to a report by the Telecom Regulatory Authority of India (TRAI) the total number of internet subscribers in the country as a percentage of the overall population increased by 12.01% from December 2013 to reach 267.39 million in December 2014.

Along these lines, Mayank Kapur Co-founder of Gramener cites the increased level of data collection and the scale to which it could potentially grow as an opportunity for India in public sector AI applications: “In the public sector, we have an advantage of scale the amount of data that can potentially be gathered is huge.

Juergen Hase the CEO of Unlimit- A Reliance Group Company, one of India’s largest private sector companies, expressed his thoughts during our research: “The direct switch to mobile platforms in India means that there are no legacy systems to deal with and new technologies can be developed from scratch.” As shown in the figure to the right, an overwhelming majority of India’s Internet subscribers gain access through mobile wireless networks.

He thinks that the two underlying factors here are larger salaries lie in the corporate sector, which is potentially creating a dearth of mentors for the next generation of software developers looking to transition into AI and the availability of data.

Industry-university partnerships where students can work with real world data science applications and reskilling of existing workforces (example: getting software engineers to look at statistics or vice versa) are just beginning to take shape in India (starting with the unicorns).” The cultural factors in India play a role in talent development here as explained by Nimilita Chatterjee SVP, Data and Analytics at Equifax: “I see issues in AI talent in India are at 3 levels: The issues that Nimilita addresses above aren’t all that different from what we see in the United States (indeed in Silicon Valley) on a daily basis.

Just being cheaper than a Western idea is not true innovation… that’s not ALL that we should be thinking about.” The following points became evident through our interviews about India’s AI risks and weaknesses: In light of NITI Aayog’s recent report, and in light of our research on AI in India (and our understanding of AI’s economic possibilities in various tech ecosystems), we were determined to contribute to the national conversation about AI in India.

With the AI wave, there is the potential to catch up immediately thanks to substantial and continuing growth in internet connectivity, and India’s swollen population of young engineers could hypothetically leap directly to the cutting edge of programming, development, and data science.

If India can marshall this next generation of the tech-savvy workforce people into the right skills, they can form a huge base of just the kind of engineers and data scientists experts that the world will need most in the years ahead.

They could stay at the bottom of the value chain, basically being relegated to tagging images, combing through data for edge cases, training algorithms, etc (we might name this scenario “The nation of mechanical turks”).

being a cheaper version of some Western business model), and become the kind of firms that the rest of the world references as “leading” and “premier,” not merely “less expensive.” The services sector is where much of India’s current and future growth is likely to come from (, with IT services and business process outsourcing (BPO) services employing millions of Indians.

India’s real opportunity is doing AI for social good as we have historically always been a technology test bed for social efforts and we possess the technological know-how to get it done reasonably well here.” We certainly hope that India can make the most of artificial intelligence –

AI is coming for white-collar workers

The big picture: Much of the research assessing the workforce impact of these new technologies — robotics, AI and machine learning — lumps them all together under the bucket of automation.

But when looking specifically at AI — which has the ability to interpret voice commands, recognize images, and make predictions and decisions — jobs ranging from radiologists to legal professionals and marketing specialists could find themselves with drastically diminished roles.

But the higher-earning workers — who are likely to have more education and more diverse skills, as well as bigger bank accounts — will be far better prepared to navigate the tectonic shifts, Muro says.

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