Today most large companies are looking at the potential of AI/ML, and despite significant investments, hiring data scientists and investing time and money, data science fails to take things to the next level.

One of the biggest challenges present in AI/ML is that a large majority of models are not deployed in production. A lot of people in the enterprises have realised that typically when you have any kind of machine learning or data science work, it goes from a few weeks to develop the model, takes far longer when we talk about placing the developed models into production, maybe more than a year till the model is put into production.

The production takes a long time compared to the development of an ML model. Sometimes when you start rearchitecting the whole ML pipeline keeping deployment in mind, the entire work can go in vain. Deployment pipelines, deployment assumptions and deployment way of doing modelling is quite different. Is data science enterprise-ready?

In a Gartner’s survey of more than 3000 AI aware C-level executives, only 20% reported having AI production, and 80% said they are developing, experimenting and contemplating the use of AI. In another report by Mckinsey, the firm found that out of 160 reviewed AI use cases, 88% did not progress beyond the experimental stage.

As the market for AI technologies and techniques matures and grows, companies need more and better access to innovative AI models, applications and platforms. Unless things are in production, there is no return on investment.

“Technology innovation leaders are keen to apply DevOps principles for AI/ML projects, but they often struggle with architecting a solution for end-to-end automation pipelines across data preparation, model building, deployment and production because of lack of process and tooling know-how,” says Gartner.

Management Problems 

The management across several companies may not be fit to learn or comprehend data science. You may have the best model in the world, but if the management doesn’t realise its value, it is probably not going into production. A lot of these times, business intelligence and software stack offer clearer value to an organisation than complex data science systems. With the high expenses of developing AI projects, many organisations are reluctant to invest in the required staff and software to deliver on the promise of AI.

A lot of the times in data science, models do not survive the PoC stage and get dumped due to various challenges, which boils down to a lack of fundamental data literacy at senior levels that leads to data science getting ignored often. 

Technical Challenges

For the majority part, the reason why models are not deployed comes down to resources is that technology is new, and most IT-led companies are merely unfamiliar with the tools and specialised hardware needed to deploy data science models successfully. 

One of the essential things in data science is choosing the right problem and chasing the right solution. But, due to complicated technical details, people get caught up on and find themselves a year later having added zero value. Often in data science, projects end up being more complicated in comparison to the business value they are meant to produce. 

Data Collection Issues

According to experts like Bill Inmon, the vast majority of data scientists spend most of their time as data collectors, consolidating disparate data sources together, and formatting and cleaning data. Data sourcing, understanding, organising, cleaning are the most difficult part of most AI projects. 

Most organisations have highly siloed data which makes it very difficult to put a model in production. Not just data, ML pipelines also take place in isolation and not in a connected manner. This leads to a lack of collaboration among the team members.

Collection of the required data is a challenging task. Data always exists in different formats, structured and unstructured, video files, text, and images, stored in various places with unique security and privacy issues, which makes implementing AI challenging, because the data needs to be consolidated and cleaned. Unstructured data or unformatted data which may take most of the time for data cleaning and can be a reason for losing motivation. Insufficient data which is available for the analysis can also be a factor for failed AI projects. 

Incompatibility With Enterprise Systems

Data scientists use languages like Python that may not be compatible with the programming languages used in production systems. To make the model work with the existing systems, it takes a lot of time before the model is recoded, fully retested and tested before deployment. This process may take months and by the time the model is set for production, it can become unnecessary.

If a data science team deployed a model in production, it might need them to work with an engineer to implement it in Java or some other programming language to make it work for the enterprise. Now, this needs constant iterative effort as the model can become useless otherwise with the addition of new data.

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ABBYY, a Digital Intelligence company, today launched a global initiative to promote the development of trustworthy artificial intelligence (AI) technology. As AI becomes ubiquitous across consumer and enterprise high-value and large-scale uses and more open source tools become available for digitizing data, the ethical use of accessing and training data is imperative.

A growing number of technology leaders expect fair, transparent and ethical AI systems in order to fuel the continued adoption of AI and spur innovation. In fact, by 2025, Gartner estimates 30 percent of large enterprise and government contracts for the purchase of digital products and services that incorporate AI will require the use of explainable and ethical AI. Furthermore, three-fourths of consumers say they won’t buy from unethical companies, while 86% say they’re more loyal to ethical companies. Therefore, ABBYY made public its core guiding principles on developing, maintaining and promoting trustworthy AI technologies and advocates for other technology leaders to do the same.

“Innovation and ethics go hand in hand. As the use of AI proliferates, it is important for technology leaders to adhere to and promote the utilization of technologies that are transparent, fair, unbiased and respect data privacy,” commented Anthony Macciola, Chief Innovation Officer at ABBYY. “By adhering to high standards with regards to the performance, transparency and accuracy of our products, we are able to deliver solutions that have a tremendous impact for our customers.”

• ABBYY, whose Digital Intelligence solutions leverage AI technologies including machine learning (ML), natural language processing (NLP), neural networks, and optical character recognition (OCR) to transform data, affirmed its commitment to the following principles and advocates for other leading technology organizations to also commit to trustworthy AI standards:
• Incorporating a privacy-by-design principle as an integral part of its software development processes
• Protecting confidential customer and partner data
• Developing AI technologies that meet or exceed industry standards for performance, accuracy and security
• Empowering customers and partners to successfully implement digital transformation in their organizations by delivering solutions that provide a greater understanding of content and processes
• Providing visibility into the performance characteristics and metrics of its technologies, as well as providing opportunities for product feedback
• Delivering AI technologies that are socially and economically beneficial
• Fostering a culture that promotes the ethical use of AI and its social utility

“AI has the power to yield significant social and economic benefit,” continued Macciola. “With ethics in mind, we have the ability to transform the future in a manner that promotes innovation, accelerates technological advancements, and augments human intelligence, creativity, and capabilities responsibly.”

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Microsoft has promised that by 2030 it will be carbon negative, and by 2050 it will have removed all the carbon it has emitted since its founding in 1975.

To become negative, the company will have to not just reduce its carbon emissions and shift to renewable resources, but actually offset its carbon footprint.

The challenge ahead

With the science behind anthropocentric climate change settled, customers and companies face increasing pressure to reduce emissions.

There are three commonly classified categories of emission: Scope 1, those directly from a person or company’s activities; Scope 2, those indirectly created by the production of the electricity or heat used; and Scope 3, those indirectly created by all other activities (such as food production, or manufacturing of goods used).

For 2020, Microsoft – currently worth $1.27 trillion – expects to emit 100,000 metric tons of Scope 1 carbon, four million tons of Scope 2 carbon, and 12m tons of Scope 3.

“Historically we’ve focused on Microsoft’s scope 1 and 2 emissions, but other than employee travel, we haven’t calculated as thoroughly our scope 3 emissions,” company president Brad Smith said in a blog post.

“That’s why we’re committing to becoming carbon negative for 2030 for all three scopes.”

By the middle of the decade, Microsoft expects to bring Scope 1 and 2 emissions to “near zero” by shifting to 100 percent renewable energy supply via power purchase agreements for the electricity consumed by its data centers, buildings, and campuses. It will electrify its global campus operations vehicle fleet by 2030, and “pursue” International Living Future Institute Zero Carbon certification and LEED Platinum certification for our Silicon Valley Campus and Puget Sound Campus Modernization projects.

It will aim to reduce its Scope 3 emissions by more than half by phasing in an internal carbon tax from July 2020. The fee will start at $15/metric ton, covering Scopes 1 and 2 and Scope 3 travel emissions. “Our fee is paid by each division in our business based on its carbon emissions, and the funds are used to pay for sustainability improvements,” Smith said.

All other Scope 3 emissions will be charged via a lower, undisclosed, carbon fee – which will eventually rise to the same level.

By July 2021, the company plans to implement a procurement process that incentivizes emissions reductions among suppliers, and encourages accurate reporting.

By 2030, Microsoft will then start using negative emission technologies “potentially including afforestation and reforestation, soil carbon sequestration, bioenergy with carbon capture and storage (BECCs), and direct air capture (DAC),” Smith said. The company expects to first focus on nature-based solutions, but shift to technology-based ones if and when they improve.

To help those technologies develop, Microsoft plans to invest $1 billion over the next four years “into new technologies and expand access to capital around the world to people working to solve this problem,” Smith said. “We understand that this is just a fraction of the investment needed, but our hope is that it spurs more governments and companies to invest in new ways as well.”

He added: “We’ll focus our funding on investments primarily based on four criteria: (1) strategies that have the prospect of driving meaningful decarbonization, climate resilience, or other sustainability impact; (2) additional market impact in accelerating current and potential solutions; (3) relevance to Microsoft by creating technologies we can use to address our unpaid climate debt and future emissions; and (4) consideration of climate equity, including for developing economies.”

Not to be negative

“While there is a lot to celebrate in Microsoft’s announcement, a gaping hole remains unaddressed,” Greenpeace senior campaigner Elizabeth Jardim. “Microsoft’s expanding efforts to help fossil fuel companies drill more oil and gas with machine-learning and other AI technologies.”

Along with the other major cloud providers, Microsoft continues to pursue lucrative contracts with fossil fuel companies, with an Azure division dedicated to the sector.

In the customer stories section of its website, among those featured are BP, Shell, Eni, Petrofac, ExxonMobil, Chevron, Oilfield Solutions, and Volga Gas.

“Visualize reservoir simulations to increase drilling hit rates using high-performance computing (HPC),” Microsoft’s ‘Azure for the energy industry’ page states. “Improve decision-making and reservoir production using IoT drilling sensors and advanced analytics,” it adds.

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It is surprising how much can the world change in a few years. Today, machines can see (a field popularly known as Computer Vision), they can hear (Hi Alexa!) and understand our language (field called Natural Language Processing) to a good degree. And they are only going to get better from here.

On some specific tasks, machines actually perform better than humans. This includes identifying objects in images or doing video surveillance without getting tired. We have seen drastic improvements in virtual assistants, chatbots or use of voice based systems for customer service.

If these developments make you wonder what is driving these changes then you are not alone. All of these developments have been fueled by the power of Data Science. Data Science is nothing but the science of extracting insights from data. With ongoing digitalization, dropping storage costs and increasing compute power, the barriers to use Data Science have come down significantly in the last decade.

Some of the latest developments in Data Science:

1. Machine Learning – Machine Learning is a field in Data Science where we train machines to find patterns and insights on its own and then use that to solve problems for us.

For example, using data to identify which customers are likely going to churn in next 3 months based on latest insights from data is an example of machine learning.

2. Deep Learning – Deep Learning is a special class of machine learning algorithms, which were inspired by how the human brain works. Deep learning has made a significant impact on several real life problems and we are already using it in a lot of products we use on a day to day basis.

For example, text prediction in smartphones to Gmail, or cameras identifying faces by themselves is driven by deep learning.

3. Natural Language Processing – Algorithms which helps machine understand human language and what we mean are part of techniques commonly known as Natural Language Processing. This helps news companies to create summaries of articles to tracking customer sentiment on social media.

4. Computer Vision – Videos and images are nothing but a different form of data. Algorithms which work to help machines see and interpret images (and videos) are popularly known as computer vision. Detecting workers without proper safety gear in a manufacturing plant is an example of computer vision.

Now, a lot of these techniques have been around for some time. But when these techniques are supplied with large amounts of data and compute power, magic starts to happen. This is why we are seeing a whole new set of applications / software becoming smart and changing user experience.

The impact from these technologies increases significantly when we combine these fields to create complex and automated systems. These systems can perform all those tasks which were historically associated with humans.

The coming decade

In the coming decade, we will see a whole range of new products which will increasingly do work we believed only humans could do for a long time. This is liberating for some of us and also challenging for a lot of us as new applications would improve our lives, but at the same point there will also be job replacements at a large scale.

But whatever happens, one thing is for sure that the coming decade is going to be exciting and Data Science will be fuelling the future for the coming years!

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Despite the widespread adoption of AI, there’s one misconception that still affects Machine Learning enterprises. Consumers wary of new Machine Learning technology – 72% of Americans, according to one study – often have a notion of AI as a monolith, with a single all-knowing algorithm controlling everything from our self-driving cars and smart refrigerators to the very stars and beyond.

Of course, AI isn’t an omnipotent single entity, and its uses vary greatly even within a single industry. In fact, the way one company develops its own algorithms for Machine Learning may work best collaboratively, exploiting other organizations’ tech expertise, domain knowledge, and workforce – the fundamental pillars of usable AI.

Companies that work collaboratively not only have a chance to right the public’s perception of AI, but also increase their effectiveness via partnerships. Here are some of the ways bringing AI companies together increases results beyond what’s possible when working alone.

The Varying AI Skill Sets

Any groundbreaking technological change – from the first Industrial Revolution to the adoption of personal laptops in business – creates a fear that current low-skill workers will be left behind. The concept of an AI replacing these jobs –  with supervision from a few high-skilled workers – is easy to grasp. It’s also wrong.

According to the World Economic Forum, AI is expected to replace 75 million jobs with 133 million new ones. And while these jobs aren’t a one-to-one exchange – lost data entry jobs do not directly translate into data analyst positions – the fact remains: AI needs people of all skill levels working in concert to achieve its promise.

A company like CloudFactory enables thousands of workers in developing countries to have access to the fast-growing AI job market. Their trained workforce is the human-in-the-loop face of AI. Yet such a workforce needs to have access to a world-class set of AI-powered software tools that enable workers to effectively train their Machine Learning models. These tools need to be cutting-edge, but also approachable to the average worker. Deepen AI, a company with deep expertise in developing best-in-class annotation tools, was the perfect fit to partner with in order to bring the world the benefits of Human-AI collaboration.

It’s this type of partnership between companies that benefits clients, workers, and the end consumer as well. A multifaceted approach to collaboration improves outcomes across the board.

Humans and AI: Working In Tandem

Self-driving cars, Medical Imaging diagnosis, Predictive Intelligence – in all the fields where AI stands at the forefront, the human element behind these technologies remains just as important. It’s only with the guidance of experienced workers – be they laborers, technicians, engineers or otherwise – that these AI technologies come into their own via greater adoption.

The collaboration between AI companies is, in a notable way, similar to the relationship between humans and AI. Companies that strike partnerships rely on each other’s tactics and data to improve Machine Learning models and create new opportunities for their employees. It’s a mutually beneficial collaboration not entirely unlike, say, a hospital using Machine Learning to aid in disease diagnosis.

As AI grows to affect more and more industries, the rate of adoption will be affected by how comfortable users and customers are with working alongside the tech. This is done through making the tools more accessible, but also by emphasizing the power of working with AI, rather than against it.

The Difference In Our Daily Lives

Partnerships between AI companies go a long way toward helping the public understand the tremendous benefits. When AI tech is developed in a way that brings both humans and AI companies together, the breakthroughs are far more innovative than before.

In a partnership like that between Deepen and CloudFactory, customers have the chance to see just how powerful AI can be when implemented in a collaborative manner. These sorts of alliances help to demystify AI in a way consumers not only understand but appreciate.

When AI and humans work together, the results end up beyond what we currently expect. It’ll take more collaboration between AI companies to get there, but the fruits of this labor are there for companies willing to work together to reach them.

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Humans and machines are destined to live in an ever-closer relationship. To make it a happy marriage, we have to better address the ethical and legal implications that data science carry.

Artificial intelligence, and in particular its subfields of machine learning and deep learning, may only be neutral in appearance, if at all. Underneath the surface, it can become extremely personal.

The benefits of grounding decisions on mathematical calculations can be enormous in many sectors of life. However, relying too heavily on AI inherently involves determining patterns beyond these calculations and can therefore turn against users, perpetrate injustices and restrict people’s rights.

AI in fact can negatively affect a wide range of our human rights. The problem is compounded by the fact that decisions are taken on the basis of these systems, while there is no transparency, accountability and safeguards on how they are designed, how they work and how they may change over time.

Encroaching on the right to privacy and the right to equality

The tension between advantages of AI technology and risks for our human rights becomes most evident in the field of privacy. Privacy is a fundamental human right, essential in order to live in dignity and security. But in the digital environment, including when we use apps and social media platforms, large amounts of personal data is collected – with or without our knowledge – and can be used to profile us, and produce predictions of our behaviours. We provide data on our health, political ideas and family life without knowing who is going to use this data, for what purposes and why.

Machines function on the basis of what humans tell them. If a system is fed with human biases (conscious or unconscious) the result will inevitably be biased. The lack of diversity and inclusion in the design of AI systems is therefore a key concern: instead of making our decisions more objective, they could reinforce discrimination and prejudices by giving them an appearance of objectivity. There is increasing evidence that women, ethnic minorities, people with disabilities and LGBTI persons particularly suffer from discrimination by biased algorithms.

Studies have shown, for example, that Google was more likely to display adverts for highly paid jobs to male job seekers than female. Last May, a studyby the EU Fundamental Rights Agency also highlighted how AI can amplify discrimination. When data-based decision making reflects societal prejudices, it reproduces – and even reinforces – the biases of that society. This problem has often been raised by academia and NGOs too, who recently adopted the Toronto Declaration, calling for safeguards to prevent machine learning systems from contributing to discriminatory practices.

Decisions made without questioning the results of a flawed algorithm can have serious repercussions for human beings. For example, software used to inform decisions about healthcare and disability benefits has wrongfully excluded people who were entitled to them, with dire consequences for the individuals concerned.

Stifling freedom of expression and freedom of assembly

Another right at stake is freedom of expression. A recent Council of Europe publication on Algorithms and Human Rights noted for instance that Facebook and YouTube have adopted a filtering mechanism to detect violent extremist content. However, no information is available about the process or criteria adopted to establish which videos show “clearly illegal content”.

Although one cannot but salute the initiative to stop the dissemination of such material, the lack of transparency around the content moderation raises concerns because it may be used to restrict legitimate free speech and to encroach on people’s ability to express themselves.

Similar concerns have been raised with regard to automatic filtering of user-generated content, at the point of upload, supposedly infringing intellectual property rights, which came to the forefront with the proposed Directive on Copyright of the EU. In certain circumstances, the use of automated technologies for the dissemination of content can also have a significant impact on the right to freedom of expression and of privacy, when bots, troll armies, targeted spam or ads are used, in addition to algorithms defining the display of content.

The tension between technology and human rights also manifests itself in the field of facial recognition. While this can be a powerful tool for law enforcement officials for finding suspected terrorists, it can also turn into a weapon to control people. Today, it is all too easy for governments to permanently watch you and restrict the right to privacy, freedom of assembly, freedom of movement and press freedom.

What governments and the private sector should do

AI has the potential to help human beings maximise their time, freedom and happiness. At the same time, it can lead us towards a dystopian society. Finding the right balance between technological development and human rights protection is therefore an urgent matter – one on which the future of the society we want to live in depends.

To get it right, we need stronger co-operation between state actors – governments, parliaments, the judiciary, law enforcement agencies – private companies, academia, NGOs, international organisations and also the public at large. The task is daunting, but not impossible.

A number of standards already exist and should serve as a starting point. For example, the case-law of the European Court of Human Rights sets clear boundaries for the respect for private life, liberty and security. It also underscores states’ obligations to provide an effective remedy to challenge intrusions into private life and to protect individuals from unlawful surveillance. In addition, the modernised Council of Europe Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data adopted this year addresses the challenges to privacy resulting from the use of new information and communication technologies.

States should also make sure that the private sector, which bears the responsibility for AI design, programming and implementation, upholds human rights standards. The Council of Europe Recommendation on the roles and responsibilities of internet intermediaries, the UN guiding principles on business and human rights, and the report on content regulation by the UN Special Rapporteur on the promotion and protection of the right to freedom of opinion and expression, should all feed the efforts to develop AI technology which is able to improve our lives. There needs to be more transparency in the decision-making processes using algorithms, in order to understand the reasoning behind them, to ensure accountability and to be able to challenge these decisions in effective ways.A third field of action should be to increase people’s “AI literacy”.

A third field of action should be to increase people’s “AI literacy”. States should invest more in public awareness and education initiatives to develop the competencies of all citizens, and in particular of the younger generations, to engage positively with AI technologies and better understand their implications for our lives. Finally, national human rights structures should be equipped to deal with new types of discriminations stemming from the use of AI.

Artificial intelligence can greatly enhance our abilities to live the life we desire. But it can also destroy them. We therefore have to adopt strict regulations to prevent it from morphing in a modern Frankenstein’s monster.

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Artificial intelligence (AI) and machine learning have a lot to offer to marketers. In addition to simplifying several mundane tasks, this technology might finally help marketers achieve relevance at scale and ensure “the most pertinent content reaches the most promising customers at the moments of greatest influence across multiple channels and markets.”

Many marketers have faith in AI’s contribution to their field, but not many are implementing it. Eighty-five percent of executives believe that AI technologies will help their companies gain more competitive advantages, yet only 10 percent are using AI today.

If you are among the majority of companies not using AI and you’re not sure where to start, here are nine areas where machine learning is making the life of marketers easier.

1. Better marketing campaigns

One of the biggest benefits of machine learning is that it can recognize patterns and connections that humans can’t. This feature is useful when we want to predict purchase trends and user behavior patterns. With machine learning models to analyze Adobe Analytics or Google analytics data, you can use behavior data from across the web to find and target the audience that is most interested in your product, and, therefore, most likely to convert.

2. Churn prediction

Predicting customer churn is hard but not impossible. The signs are there if you can spot them, and AI certainly can. Based on historical data, machine learning algorithms can analyze behavior, identify customers who are most likely to churn, and even trigger necessary actions to prevent that from happening (special offers, check-up e-mail, etc) – all without human involvement.

A lot of churn prediction revolves around the customer lifetime value score (CLV). Traditionally, it is calculated based on a few simple metrics, but some companies have taken it a step further and built their model using predictive behavior modeling and microsegmentation. The results show which of your customers are most likely to churn in real time.

3. Improving customer experience

Throughout the customer journey, customers are exponentially more in touch with AI rather than with humans. Apart from marketing automation, chatbots are becoming a staple feature of business interaction. Not only can they answer general questions and troubleshoot problems, they can also help you pick products based on your preferences.

1-800-Flowers is using GWYN, an extension of IBM’s Watson, to help customers pick flower arrangements based on the occasion and intended audience. This alone increased the company’s revenue by 6.3 percent in the first quarter of 2017. North Face has embedded chatbots into their website. Chatbots ask customers where and when they are going and suggest products from the catalog that fits those needs. Hilton Hotels are piloting a new concierge experience using chatbots.

4. Dynamic pricing

Dynamic pricing allows companies to be more agile and respond more adeptly to supply and demand fluctuations.

Amazon and eBay already tweak their prices daily using algorithms that change the prices of products according to the loyalty level of customer. It’s not only reserved for online stores. Big retailers are slowly embracing dynamic pricing in physical stores. Some cafes and restaurants offer discounted lunch meals for those who want to avoid the midday rush or discounted prices in the evenings to cut food waste.

5. AI-generated content

This is a fairly new area for AI. While computers still can’t write an in-depth article about something industry specific, a few companies are offering simple content-generation tools that transform numeric data into coherent narratives or that generate lengthy reports within a matter of minutes instead of weeks.

In a world that is bursting at the seams with fast, meaningless content, this new possibility opens doors to provide quality content that typically takes a long time to generate.

6. Better customer segmentation

Clustering algorithms can help marketers add more dimensions to the analysis of their customer data, thereby creating a richer picture of clients. Clustering algorithms also adjust constantly as more data becomes available, which makes it more responsive than traditional segmentation techniques.

7. Personalization

It’s impossible to talk about machine learning in marketing without mentioning what it can do for personalization of content and campaigns. Netflix and Amazon are doing it already. Algorithms can re-engage based on specific customer behavior with incentives like offers and discounts, but with the help of machine learning, ads and campaigns can be customized differently to each customer segment, even down to the pictures that are featured as well as taglines, headers, and formatting.

More companies are using recommendation engines as a part of their personalization strategy, finding that they are a powerful tool for increasing customer loyalty and maximizing customer lifetime value. This wide use of recommendation engines has recently become possible thanks to decreasing prices in the software, data storage and the data itself.

8. Content optimization

Machine learning has the opportunity to replace A/B testing of content. With A/B testing, there is always a period of time where revenue is lost due to the use of the lower-performing version of the content. With new platforms available, multiple options of content can be tested simultaneously, and traffic from the lowest-performing campaigns is redirected automatically to better-performing options while still providing you with insights.

9. Monitor media presence

A logo placed on a billboard or a T-shirt is not only seen by bypassers anymore. Thanks to social media, a company’s logo can show up anywhere and have a totally different impact than expected. AI can help companies track and measure brand exposure during commercials, events shown on TV or social media. Companies are applying computer vision to pictures, videos and social media feeds to give a complete picture of traditional media outreach.

Machine learning applications have the potential to transform the marketing strategies many companies are using. This process doesn’t always have to evolve from inside the firm’s marketing department; there are companies that help marketers solve common problems in a more effective way through constantly improving predictive analytics models with the help of machine learning. This way, the marketing automation trend is only going to grow, providing marketers with more opportunities to be productive, customer-oriented and data-driven.

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If you’re thinking about implementing artificial intelligence (AI) into your legal organization, congratulations on being a forward thinker. Although … it’s actually not as forward thinking as it may seem. AI is no longer the nebulous, otherworldly techno-universe that you many have once envisioned. It’s already here, today, giving us directions, telling us jokes, recommending music, answering our questions, generally making our jobs – and lives – easier. Here are seven things to keep in mind about AI as you prepare to implement it in your legal organization:

1. It’s all about the data.
Artificial intelligence is built on data. Therefore, the effectiveness of an AI solution can only be as good as the accuracy of the data it is relying on. That’s why you can’t just decide to “do AI.” You first have to identify the problem you are trying to solve, then take a look at the data (for example, electronic billing data or case and matter management data from a matter management system) to see if it’s “clean” or if it needs some data hygiene.

2. AI is not just one technology.
If you’re searching for the next big thing in AI, you’re not going to find it. AI is not a singular thing. There’s no “killer” AI app for the legal industry. Instead, there are AI applications in many areas of the legal industry, and each of those applications might use a different AI-related technology.

3. It’s not magic, it’s just software. 
While the term artificial intelligence has a mystic, futuristic aura about it, in reality it’s basically just computer software. Sure, it’s software created by really smart technology engineers and product developers who integrate complicated algorithms to compute calculations … but in the end, it’s just one of the tools legal professionals have at their disposal to help them work more efficiently.

4. AI can help you run a business.
It’s the business side of being a lawyer where AI technologies are most helpful. Legal organizations have the same kind of business processes as any type of business, such as billing, pricing, and marketing, etc. Most of those processes involve numbers and data (prices, margins, budgets, expenses, etc.). And remember, it’s all about the data, so all of these business processes can be analyzed and managed with the help of AI technologies.

5. AI does not replace humans, it assists them.
Attorneys are not going to become obsolete, replaced by robots. Sure, AI solutions can take in the data and make predictions or suggest likely outcomes, but those predictions are of varying degrees of certainty. And the conclusions may be based on inaccurate data. That’s where human lawyers come in. They evaluate the data, draw upon past experiences that may or may not be part of the data, and generate their own answers, predictions, and advice – all informed by (not determined by) artificial intelligence.

6. Adopting AI means embracing change.
If you intend to implement AI technologies into your legal organization, you must be ready for change.
Not only will your processes and workflows need to change to incorporate AI into the business, but you’ll also likely be working with a whole new set of people. Whether they are part of your firm or outside consultants, expect to collaborate with data analysts, process engineers, pricing specialists, and other data-driven professionals.

7. Clients will drive your need for AI. 
Speaking of collaboration, AI can also be a catalyst for collaboration between a law firm and its clients. For starters, clients’ needs will often drive the adoption of AI solutions. And in many cases, it’s the clients who have the data needed to build effective AI solutions.

The post 7 Things Lawyers Should Know About Artificial Intelligence appeared first on Artificial Intelligence.

MemSQL, provider of the fastest real-time data warehouse, today announced the results of a survey commissioned with O’Reilly Media on the adoption of artificial intelligence (AI) and machine learning (ML) in the workplace. According to the survey of over 1,600 respondents, 61 percent, regardless of company size, indicated ML and AI as their companies’ most significant data initiative for next year, when asked to pick from several options likely to be important concerns in today’s climate. With big data and business analytics initiatives coming in at a close second (58 percent).

The majority of respondents (88 percent) indicated that their company already has, or has plans to, implement AI and ML technologies within their organization. Of those planning to implement ML/AI, the respondents appear eager for these implementations, and 95 percent indicated it would either complement or make it easier to do their job rather than reducing or making their role harder.

Additional Key Survey Findings

• 65 percent of respondents using, and planning to use, ML/AI cited that a key aspect of adopting ML and AI was to enable more informed business decision making, underscoring the importance of these technologies for analytics.
• 74 percent of all respondents consider ML and AI to be a game changer, indicating it had the potential to transform their job and industry.
• Of those indicating they actively use ML and AI, 58 percent indicated they ran models in production.
• The findings also suggest that uses of such technologies are evolving rapidly, with 77 percent of respondents actively using ML/AI indicating that creating new models was part of their short-term goals.

Based on these priorities, those indicating they were data scientists more commonly reported being part of a company having ML and AI already in place. Moreover, among the organizations not currently using ML and AI, the responses suggest it is a lack of data science talent that most impedes them from using these technologies, this is especially the case amongst those that reported they were at smaller companies.

As ML and AI technologies spread through organizations, the need for data scientists and other technical professionals is growing. To make the jobs of these professionals easier, they will need to assess the technology infrastructure stacks already in place to support the new technologies.

These survey results underscore the need for an intelligent database that can support advanced workloads. MemSQL is the database for transactions and analytics at scale, allowing data scientists to do model and operationalize advanced computations quickly. MemSQL has native integrations with Apache Kafka, including exactly once semantics, and a high performance parallel connector to Apache Spark.

To learn more, please review the complete survey results memsql.com/mlsurvey. To learn more about MemSQL, visit memsql.com to get started on modernizing your data and application infrastructure.

Survey Specs
The survey was conducted from October to November 2017, and was answered by 1624 respondents with various job functions including developers, data scientists, and data analysts.

About MemSQL
MemSQL envisions a world of adaptable databases and flexible data workloads – your data anywhere in real time. Today, global enterprises use MemSQL as an intelligent database to cost-effectively ingest data and produce industry-leading time to insight. MemSQL works as a managed service, on any cloud or on-premises.

The post Survey Finds Machine Learning and Artificial Intelligence are Top Business Priorities appeared first on Artificial Intelligence.

Technology today is forcing significant changes in the ways we communicate. Some technologies, like artificial intelligence, improve efficiencies (speed, quality, style) in our daily lives that many of us likely don’t even notice are happening. Similar to any technology that’s new to an industry, AI has caused some skepticism, and I’ve witnessed that the media industry in particular has been prone to these beliefs that AI is replacing jobs. But in an era where unique and real storytelling is valued more than ever, AI can be a powerful tool for publishers, brands and anyone else who aims to create engaging content in a sustainable, consistent and scalable way.

AI Is Gradually Transforming Content Creation

In the early days of AI, it was perceived by many as a complex technology. Because of its highly technical nature, developers and engineers were among the few who fully understood how to use AI and how it could be applied in the real world. In the media industry, new technologies are traditionally eased into newsrooms — from CMS tools to analytics and performance tracking. But change can be scary, and many live with the “if it’s not broken, don’t fix it” mentality. Adjusting everyday processes that have been the foundation of jobs for years can seem daunting and, even worse, less efficient.

Everyday AI is gaining traction in content creation processes as creators become more savvy and open-minded about how this technology can help them tell great stories, like turning text into video, for example. Larger media companies like Reuters (Full disclosure: Reuters is a Wibbitz customer) recognize the benefits of leveraging AI for video creation, enabling the company to produce video at the same pace as editorial content.

Beyond text-to-video, we’re seeing a lot of AI technologies used in data-rich domains like weather, sports and finance. Companies like Automated Insights and Graphiq are able to ingest a large amount of data, understand the context, identify key story points and generate text articles or infographics. Across mediums, AI streamlines the storytelling process by eliminating the time-consuming, non-creative tasks that burden creators and it’s becoming a key tool for storytellers to consistently hit the right tone and keep pace with demand.

AI Complements Without Replacing

Despite these benefits, AI carries a negative stigma that it’s going to replace humans altogether. However, it’s changing the way content creators work and for the better. Without disputing the notion that AI reduces or eliminates many everyday tasks, it’s important to understand it from a more positive point of view: It’s making workflows more efficientand helping to drive new media formats.

AI is now so sophisticated that it can do a significant portion of the legwork — as in detecting what is trending and happening around us — and it sets up its human allies for success. AI completes these tasks at a scale that humans are incapable of, but then storytellers have the level of detail, cultural sensitivity and personality that brings the content to life. Merged together, this process makes storytellers better at their jobs and proves AI as more of a complement than a replacement for modern storytelling.

Similar to other breakthrough developments that we’ve seen over the past few decades (e.g., smartphones, virtual reality, even facial recognition, to name a few), giving up partial control to technology is essential to the way we’ll interact with one another in the future. It’s unrealistic to believe that in the next decade we’ll be living in a fully machine-operated society, but it’s fair to say that related technologies will be a big part of the way we communicate, consume information and more.

If there’s one thing we know for certain, it’s that AI is not the last transformational technology coming to storytelling, as we’re already seeing progression with virtual and augmented reality. It’s the creative people who see the world differently, who are brave enough to embrace these technologies that will see the positive impact on their lives and their jobs.

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