AI News, BOOK REVIEW: How To Create An AI (Artificial Intelligence) Startup artificial intelligence

How much does artificial intelligence (AI) cost in 2019?

There has been so much talk about Machine Learning and Artificial Intelligence lately that it has become obvious – they are drastically changing the world.

Due to how promising these technologies are and the number of benefits they already deliver, many companies are willing to use these solutions for their business transformation.

Artificial Intelligence (AI) focuses on computer systems capable of performing tasks that usually only humans could do – such as decision-making, visual perception, speech recognition and so on.

By processing large quantities of submitted data, ML recognizes various patterns, and therefore builds new analytical models.

The state-of-the-art technology becomes pervasive in our lives as it starts to be widely adopted by many companies across different industries.

It has also been seized as an opportunity by marketers – machine learning chatbots prove to be effective at generating leads and providing enhanced customer experience.

At this stage, the project team defines success factors (expected metrics improvements), applicable technological stack, timeline and budget, and reflect them in the corresponding documentation.

The parties find out whether or not the AI concept is possible, and if it is, define the scope of work needed for the next move, namely prototype development.

If all critical data, processes, and metrics are available in the required format, the phase takes up to 5-7 working days on average.

Its form depends on the project complexity and tools (screen generators, application simulation programs, or design tools) used to develop it.

The risks and costs associated with software implementation are significantly reduced, as the requirements are well-discussed early on before development begins.

The MVP relies on the client’s actual data and is exposed to a small group of real customers as a simplified version of the ultimate product solution.

Data Issues The development of a reliable ML-system depends not only on excellent coding – the quality and quantity of the training data play a pivotal role.

First of all, large representative data sets are required to reasonably capture the relationship that may exist between input and output features.

Algorithm’s Performance The sufficient algorithm performance is another key cost-effective factor, as often a high-quality algorithm requires a round of tuning sessions.

However, a 90.9% accurate system aimed at diagnosing a disease, with treatment being lethal to false-positive patients, is by far not satisfactory.

Hiring For The AI (Artificial Intelligence) Revolution - Part I

In the coming years, Artificial Intelligence (AI) is likely to be strategic for a myriad of industries.But there is a major challenge: recruiting.

Simply put, it can be extremely tough to identify the right people who can leverage the technology (even worse, there is a fierce war for AI talent in Silicon Valley).

think it's critical for 'AI' teams (natural language processing, machine learning, etc.) to have a mix of backgrounds -- hiring Ph.D's and academics who are thinking about and building the latest innovations, but combining that with individuals who have worked in a business environment and know how to code, ship product and are used to the cadence of a start-up or technology company.

Increasing sophistication in automating key aspects of building, training and deploying AI models (such as model selection, feature representation, hyper parameter tuning, etc.) mean the skillset needed must be focused on model lifecycle and model risk management principles to ensure model trust, transparency, safety and stability.

Guy Caspi, CEO and co-founder at Deep Instinct: People who have strong academic backgrounds sometimes lean towards one of two directions: either they cannot leave a project until it’s perfect, often missing important deadlines – or the opposite: they’re satisfied with basic academic-level standards that may not meet an organization’s production requirements.

Top-10 Artificial Intelligence Startups in Italy

The government released a white paper on AI in March 2018 concentrating on how it can facilitate the adoption of AI technologies into public administration, and a consortium of universities established a national laboratory for AI shortly after that.

Musixmatch also offers a large-scale lyrics and music vocabulary dataset for companies and researchers looking to unleash machine learning algorithms on all the data in order to create music recommendations, analyze lyrics, and gain general insights.

The company’s AI service provides Natural Language Processing (NLP) algorithms that analyze lyrics to reveal the structure, meaning, and emotional content of songs, and they can also find the similarities between concepts and sentiments expressed in different songs.

We first came across this next startup in our article on 8 Top-funded Facial Recognition Startups. Founded in 2013, Firenze startup Cynny has raised $14 million to develop an adaptive video platform that recognizes the gender, age, and emotions of users.

Although Cogisen’s platform can be applied to a number of problems, the company has perfected its analytical capabilities for temporal information (meaning information that changes as time moves forward) and created a compression product for video and visual content using this technology.

According to the team, “standard” AI technology analyzes each full frame in a video to discover a pattern whereas Cogisen’s algorithms are able to only look at the relevant parts of each frame, and make up the rest.

then unleashes its hungry algorithms on this raw data to come up with meaningful insights provided in the form of dashboards. For example, it might raise this alert for an accommodation owner: This year’s Berlin film festival will attract 20% more travelers than last year.

Founded in 2001, Piazzola sul Brenta startup THRON has raised $7 million in funding to create a Digital Asset Management (DAM) marketing application that classifies digital content, distributes it through company channels, and autonomously chooses which pieces of content to show users.

The tool is used by marketing folks who can manage all digital content (images, video, audio, etc.) centrally, independent of file type, while the AI takes care of classification and searchability.

Founded in 2014, Milan startup ELSE Corp (which stands for Exclusive Luxury Shopping Experience) has raised $2.7 million to develop a fashion technology platform that includes virtual retail, cloud manufacturing, and AI-based product design customization.

Targeting brands, retailers, manufacturers, and independent designers, ELSE offers mass product personalization, virtual 3D e-commerce solutions including virtual fitting, and order generation for hybrid and distributed manufacturing of apparel and footwear.

The company’s algorithms analyze different asset classes (like stocks, bonds, etc.) and extract information signals from the background noise of the markets, then build actionable investment strategies on them, exploiting market inefficiencies.

A customer segmentation tool enriches customer data and helps predict buying intentions, while an audience analytics tool optimizes marketing content and engagement with the right influencers.

Axyon’s SynFinance tool analyzes the entire current and historical syndicated loan market providing pricing trends, volumes, and league tables, predicts investor participation, and generates leads for refinancing, saving a huge amount of manual analysis.

The company’s neural networks and unsupervised machine learning algorithms remove cognitive biases from these markets built on information inequality and provide users with the collective intelligence of all market participants.

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AI startups to watch: The hottest machine learning startups in the UK

Artificial intelligence could contribute an additional global economic activity worth around $13 trillion by 2030, by which point around 70 percent of companies will have adopted at least one form of AI, according toa report by a 2018McKinsey Global Institute.

Given that the UK is home to eight of the top 20 European universities and 40 percent of Europe's tech unicorns already reside here, this is of little surprise.

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