These Are The Startups Making use of AI To Deal with Local weather Change

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Local weather change is essentially the most urgent risk that the human species faces immediately. Synthetic intelligence is essentially the most highly effective software that humanity has at its disposal within the twenty-first century.

Can we deploy the second to fight the primary?

A bunch of promising startups has emerged to just do that.

Each local weather change and synthetic intelligence are sprawling, cross-disciplinary fields. Each will rework actually each sector of the financial system within the years forward. There’s due to this fact no single “silver bullet” software of AI to local weather change. As an alternative, a variety of machine studying use circumstances can assist within the race to decarbonize our world.

Practically each main exercise that humanity engages in immediately contributes to our carbon footprint to some extent: constructing issues, shifting issues, powering issues, consuming issues, computing issues. Of those, there’s a subset of decarbonization initiatives to which synthetic intelligence can productively be utilized. And of these, there’s a subset for which compelling startup enterprise fashions exist.

Many billions of {dollars} of enterprise worth will probably be created within the coming years as entrepreneurs chase down these alternatives.

And make no mistake: now we have reached an vital inflection level within the relationship between local weather change and capitalism. Unprecedented quantities of capital have begun to move towards climate-related initiatives. Prior to now few years, tons of of the world’s largest firms have publicly dedicated to reaching web zero emissions and have begun to adapt their organizations’ operations accordingly. Final yr, the most important asset supervisor on the earth introduced that it was making local weather change central to its funding technique. Governments’ central banks have even begun to take direct coverage motion as they arrive to view local weather change as a systemic financial danger.

In brief, tackling local weather change just isn’t solely an pressing world crucial however a large enterprise alternative. As investor Chamath Palihapitiya memorably predicted: “The world’s first trillionaire will probably be made in local weather change.”

You will need to point out that, along with serving as a software within the battle towards local weather change, machine studying does itself contribute to local weather change given its voracious urge for food for computing sources and thus electrical energy. This is a vital ongoing problem for the AI neighborhood, one which now we have beforehand lined on this column. This text will deal with the ways in which machine studying can assist fight local weather change.

Local weather Intelligence

One motive that firms are making local weather change a strategic precedence is that opposed climate occasions can and more and more will wreak havoc on enterprise’ core operations: disrupting provide chains, forcing mass evacuations because of wildfires, flooding services close to coasts, halting outside exercise because of excessive warmth, guaranteeing areas much less liveable.

“Individuals are starting to know that local weather danger just isn’t one thing within the distant future, it’s one thing that’s taking place immediately,” stated MSCI’s world ESG head Oliver Marchand.

So as to put together for and reply to those climate-driven disruptions, organizations want particular, actionable intelligence concerning the dangers that they face. The quickly altering surroundings implies that merely seeking to previous climate patterns just isn’t a dependable technique of assessing future danger.

As an illustration, a financial institution may wish to perceive the anticipated influence of elevated hurricane exercise on property harm alongside the coast because it assesses its actual property mortgage portfolio. A authorities could search to make focused investments to bolster its nation’s important infrastructure within the face of extra punishing climate situations. A world resort chain could discover it worthwhile to higher perceive long-term climate patterns earlier than deciding the place to develop new properties.

If there may be one factor at which trendy machine studying excels, it’s making predictions about complicated methods primarily based on plenty of information.

A number of “local weather intelligence” startups has emerged that supply predictive analytics platforms to allow organizations to higher anticipate and put together for excessive climate occasions. These startups typically mix machine studying with extra conventional climate modeling strategies, leveraging a mixture of publicly accessible, proprietary, and customer-specific information to coach their fashions.

One of many oldest and most well-funded of those startups is One Concern, which introduced a brand new $45 million funding spherical earlier this month. One Concern is creating a “digital twin” of the world’s pure and constructed environments to be able to dynamically and hyperlocally mannequin the consequences of local weather change, providing its prospects what it phrases “Resilience-as-a-Service.” The corporate has centered totally on the Japanese market so far.

Jupiter Intelligence is one other well-established participant on this class, with a powerful set of blue-chip prospects that features NASA, BP, Liberty Mutual Insurance coverage and the Metropolis of Miami.

One other local weather intelligence competitor to control is London-based Cervest, which raised a $30 million Sequence A final month from Marc Benioff, Chris Sacca and others. Cervest’s method is to make its local weather intelligence platform accessible to all with a freemium enterprise mannequin, a method it might hope results in community results.

Different startups on this house embody Climavision, Gro Intelligence, ClimateAI and Terrafuse AI.

A key problem that venture-backed firms on this class face is constructing choices which can be actually productized and scalable. Each potential buyer may have a novel set of local weather questions and targets primarily based on its explicit state of affairs, geographical footprint, bodily property, enterprise priorities, and so forth. This inevitably pulls these startups towards offering bespoke, professional-services-heavy options for every buyer.

Consultancies may be worthwhile companies however hardly ever obtain outsize, venture-scale outcomes. Time will inform whether or not a number of startups on this house achieve getting sufficient leverage out of software program and machine studying to construct a scalable, category-defining expertise firm in local weather intelligence.

Local weather Insurance coverage

Whereas the previous class of startups goals to assist organizations higher perceive and predict local weather dangers, one other set of rivals is utilizing AI to assist organizations defend themselves financially from these dangers. They’re doing so by innovating in one of many world’s oldest companies: insurance coverage.

Legacy insurance coverage firms battle to successfully assess and value the novel monetary dangers posed by local weather change. Because of this, protection may be prohibitively costly or altogether unavailable for a lot of varieties of local weather danger. In accordance with Aon, there’s a whopping $171 billion hole in local weather insurance coverage globally.

A bunch of latest entrants is leveraging different information sources, real-time analytics and AI to cost danger extra precisely and create novel insurance coverage merchandise for the period of local weather change.

A key business innovation on the coronary heart of many of those startups’ methods is named parametric insurance coverage. In contrast to conventional insurance coverage, which compensates policyholders for precise losses incurred, parametric insurance coverage routinely pays out a certain quantity upon the incidence of a predefined occasion like a hearth or storm. Parametric insurance coverage eliminates the necessity for insurance coverage brokers to evaluate and confirm particular person policyholder losses, as a substitute merely guaranteeing cost when sure parameters (therefore the identify) are met: for example, a sure variety of inches of rain, a sure warmth index, a hurricane of a sure depth, a sure discount in foot site visitors.

The parametric mannequin makes insurance coverage extra automated, data-driven and clear, with quicker and extra sure payouts. It requires refined information and analytics capabilities to execute successfully.

Paris-based Descartes Underwriting, based in 2018, presents parametric insurance coverage for a variety of climate-related dangers together with floods, droughts, provide chain disruptions, renewables yield, development interruptions and extra. Descartes makes use of machine studying to underwrite and monitor its parametric insurance policies, ingesting information in real-time from a wide range of sources together with satellite tv for pc imagery, stationary sensors, Web-of-Issues units, radar and sonar.

One other participant providing parametric insurance coverage for local weather danger is Arbol, which makes use of sensible contracts on the Ethereum blockchain to codify its insurance coverage insurance policies. This allows it to routinely pay out claims in two weeks or much less.

One of many buzzier startups on this class is Kettle, which presents reinsurance for local weather change danger with an preliminary deal with wildfires. Utilizing a proprietary neural community structure, Kettle claims that it could actually make hyperaccurate, hyperlocal predictions about wildfire danger, together with efficiently predicting the places of the 14 largest wildfires in California in 2020. Kettle’s AI-powered danger modeling allows it to supply extra aggressive pricing to its insurance coverage prospects, delivering 25%+ increased returns than conventional reinsurance gamers.

Different startups to observe on this class embody Understory and Cloud to Road.

Local weather change will reshape property and casualty insurance coverage within the years forward. This upheaval will create large alternatives for data-driven, AI-powered challengers to seize market share in what has historically been a staid business. Anticipate some large venture-backed winners in local weather insurance coverage.

Carbon Offsets

In simply the previous few years, tons of of the world’s largest firms representing trillions of {dollars} of market capitalization—from Amazon to Unilever, from Starbucks to Ford—have publicly dedicated to attaining web zero emissions on a specified timeline, typically by 2040 or 2050.

Any group looking for to cut back its carbon footprint has two primary methods to take action: straight eliminating emissions from its operations and shopping for carbon offsets. The latter technique, whereas controversial, is poised to play a central function within the years forward within the world battle towards local weather change.

Whereas nonetheless comparatively small, the carbon offsets market has been increasing quickly lately: from $34 million of offsets issued in 2016, to $73 million in 2018, to $181 million in 2020. Some consultants imagine the market is on the point of explosive development, with firms around the globe getting ready to go on an offsets shopping for spree. Influential local weather financier and policymaker Mark Carney has acknowledged that carbon offsets may very well be a $100 billion market by 2030.

The idea of a carbon offset is easy in precept: one occasion pays for an additional occasion, wherever on the earth, to get rid of an agreed-upon amount of greenhouse gases from the environment by means of emissions discount or recapture. Frequent examples of offset tasks embody planting timber (which absorb carbon dioxide) and financing renewable vitality infrastructure like wind generators.

In idea, if a company generates one ton of carbon emissions after which funds an offset undertaking that eliminates one ton of carbon emissions, that group has had zero general opposed influence on local weather change.

That’s in idea. In apply, carbon offsets are the supply of controversy and operational complexity.

Most essentially, some observers object to carbon offsets on the grounds that they artificially absolve emitters of their local weather sins, permitting them to throw cash on the downside whereas avoiding the arduous work of adjusting their very own actions to cut back emissions. There’s some reality to this angle: ideally, carbon offsets needs to be used solely as a ultimate step to get to carbon neutrality, after a company has diminished its personal emissions as a lot as it could actually. Offsets by themselves won’t ever get us to a zero-carbon world.

But within the instant time period, carbon offsets are an vital software that leverages the ability of the market to cut back emissions. Particularly provided that sure important human actions—air journey and heavy business, for example—are for primary technological causes unlikely to be carbon-free any time quickly, offsets are the one means for many organizations to get all the way in which to web zero emissions.

A extra tactical downside with carbon offsets is that they’re tough to operationalize at scale. Coordination issues loom massive provided that the client and the vendor may be on reverse sides of the world. Verifying the legitimacy of a carbon offset undertaking—for instance, {that a} tree has really been planted, that it will not in any other case have been planted (“additionality”), that it stays alive and continues to develop over time (“permanence”)—is operationally daunting. These challenges have restricted the dimensions of the offsets market so far.

To sort out these challenges, an thrilling new group of startups is making use of software program and machine studying to streamline, digitize and automate the carbon offsets market. These firms imagine they will unleash large pent-up demand in offsets and function the spine of a brand new multi-billion-dollar business.

Pachama and NCX (previously generally known as SilviaTerra) are two promising firms constructing AI-powered carbon offset marketplaces with a deal with forestation. Each firms apply laptop imaginative and prescient to aerial imagery and different sensor information to routinely estimate the carbon saved in forest timber and to repeatedly monitor the integrity of carbon offset tasks on their platforms.

One space during which the 2 firms differ is their method to the provision facet of {the marketplace}. Whereas Pachama selects a curated set of forestation tasks from which customers on its platform should buy offsets, NCX’s method is extra radically democratized: any particular person landowner, irrespective of how small, can be a part of the platform and promote carbon credit in change for a dedication to protect timber.

“As local weather change turns into a central subject on the agenda of companies and governments, it’s completely vital that we enhance the state of carbon offsets markets, guaranteeing integrity, transparency and accountability,” stated Pachama CEO Diego Saez Gil. “If we get it proper we may assist finance the restoration of tens of thousands and thousands of hectares of forests, eradicating gigatonnes of CO2 from the environment.”

One other firm to observe on this class is Patch, which lately raised a $4.5 million seed spherical from Andreessen Horowitz. Patch’s platform abstracts away the complexity of managing carbon offsets, making offset tasks accessible through an API and some strains of code. Behind the scenes, the corporate vets and companions with a handful of high-quality offset organizations. With its API-first method, Patch CEO Brennan Spellacy describes the corporate’s imaginative and prescient as “Plaid for decarbonization.”

One main query that these firms will face as they pursue business scale is how motivated organizations actually are to spend cash on carbon offsets. Skeptics argue that, whereas there isn’t any scarcity of company rhetoric round long-term web zero emissions objectives, many firms will hesitate to voluntarily commit vital quantities of capital to offset their emissions.

The guess that these startups (and their traders) are making is that organizations will take their web zero commitments severely and can make investments actual {dollars}, sooner quite than later, to make progress towards these commitments.

Furthermore, the whole complexion of this market may change if governments begin establishing extra rigorous mandates round carbon emission limits. This has already begun to occur in some jurisdictions. Such regulation may rework carbon offsets from a nice-to-have right into a authorized necessity for 1000’s of organizations around the globe.

Carbon Accounting

So as to cut back or offset its carbon footprint, a company should first perceive what its carbon footprint is. It is a difficult, messy, data-intensive course of.

An organization’s general carbon footprint may be damaged down into three classes: direct emissions from the corporate’s personal operations (generally known as Scope 1 emissions), emissions required to generate the electrical energy that the corporate makes use of (Scope 2 emissions), and—most difficult to measure—emissions that go into the manufacturing and consumption of the corporate’s merchandise throughout its worth chain, from upstream suppliers to downstream prospects (Scope 3 emissions).

Two illustrative examples of Scope 3 emissions: Sweetgreen contains in its Scope 3 emissions the methane emitted by cows that produce the cheese in its salads, whereas Sq. counts the carbon output from the electrical energy that particular person retailers use to energy their Sq. Registers.

Over the previous yr, a crowd of startups has emerged to supply instruments to assist organizations measure and monitor their carbon emissions, from Scope 1 to Scope 3.

These startups’ product visions transcend merely serving to firms quantify emissions. As soon as an organization has a complete view of its carbon footprint, it could actually then develop and execute on a data-driven plan to cut back its emissions: for example, switching to renewable electrical energy sources, adapting its actual property footprint, pushing its suppliers to undertake extra low-carbon practices, offering staff with data to make extra sustainable every day selections, or (as mentioned above) shopping for carbon offsets.

Probably the most high-profile firm constructing an enterprise carbon accounting platform is Watershed, lately based by a staff of Stripe veterans. Watershed has attracted buzz in no small half as a result of it’s backed by Sequoia’s Michael Moritz and Kleiner Perkins’ John Doerr; the final early-stage startup these two iconic enterprise capitalists teamed up on was Google in 1999.

“Local weather is a knowledge downside,” stated Watershed cofounder Taylor Francis. “Organizations make selections on daily basis with carbon influence—which provider to obtain from, easy methods to transport a great—but resolution makers are blind to the carbon scores of their decisions. At Watershed, we’re constructing instruments to unlock this perception and switch uncooked enterprise information—from utility payments, buy orders, transit logs, and so forth—into concrete actions that cut back carbon emissions.”

Different rivals on this fast-developing house embody Emitwise, SINAI Applied sciences, Persefoni and CarbonChain.

A elementary problem that these startups face is certainly one of information wrangling and information high quality. Scope 3 emissions specifically can show prohibitively tough to gather dependable information on.

For instance, think about a company with a fancy provide chain stretching throughout a number of international international locations, together with numerous middleman suppliers who’re unwilling to share detailed details about their operations and vitality use. Creating an in depth image of this firm’s Scope 3 emissions can be tough certainly. Because the age-old saying about information goes: rubbish in, rubbish out.

Devising an correct, repeatable, scalable technique of amassing carbon emissions information from across the globe and throughout a product’s lifecycle will probably be a key unlock for startups on this class.

It’s value noting that some startups on this class are creating carbon offset marketplaces as a pure extension of their carbon accounting platforms. This may deliver them into direct competitors with the offset startups mentioned above as these firms’ product visions converge.

And as with the earlier part, an existential query going through the startups on this class will probably be how a lot urge for food prospects need to voluntarily half with hard-earned money to be able to construct out strong decarbonization packages.


Buildings produce near one-fifth of the world’s whole carbon emissions. Making our buildings extra environment friendly is due to this fact important to the battle towards local weather change.

The excellent news is that a lot low-hanging fruit exists, as a result of buildings immediately are massively underoptimized for vitality effectivity: the carbon footprint of many current buildings may be diminished by as a lot as 90% through retrofit methods.

The one largest alternative for emissions reductions in buildings is in heating, air flow, and air-con (HVAC). Heating and cooling buildings is extremely energy-intensive immediately, accounting for roughly half of all of the vitality that buildings eat.

HVAC is a fancy, well-defined, data-rich, multivariable system: in different phrases, it’s ideally suited to optimization through machine studying (specifically reinforcement studying).

In 2016, DeepMind pioneered the appliance of machine studying to HVAC methods with a widely-discussed examine that sought to enhance vitality effectivity in Google’s large information facilities. Utilizing deep studying to optimize the info facilities’ cooling methods, DeepMind was capable of cut back the services’ general vitality consumption by as much as 40%. Contemplating that information facilities are accountable for 2% of whole greenhouse fuel emissions, this was a landmark end result.

One among Google’s information middle operators shared an instance of how DeepMind’s system labored: “It was superb to see the AI be taught to reap the benefits of winter situations and produce colder than regular water, which reduces the vitality required for cooling throughout the information centre. Guidelines don’t get higher over time, however AI does.”

A handful of startups is looking for to develop HVAC optimization options for buildings.

Montreal-based BrainBox AI claims that its software program can cut back a constructing’s carbon footprint by 20% to 40% inside a number of months by making exact, localized, real-time microadjustments to the constructing’s heating and cooling settings. BrainBox’s expertise, which doesn’t require the deployment of sensors, is reside immediately in dozens of residences, lodges, airports, nursing services and grocery shops, impacting over 100 million sq. toes of actual property in whole.

Invoice Gates-backed startup 75F additionally makes use of machine studying to observe, automate and optimize HVAC methods in buildings. Different rivals on this house embody Nomad Go, which has developed a pc vision-based answer to streamline buildings’ vitality use.

Importantly, these firms’ choices don’t simply cut back carbon emissions—in addition they minimize prices for constructing operators. BrainBox claims that its expertise saves prospects as much as 25% on their vitality payments whereas additionally bettering constructing occupant consolation by 60%. This form of incentive alignment is essential for the broad adoption of decarbonization options.

Finally, vitality consumption in buildings won’t ever be totally sustainable till the grid’s electrical energy itself comes from zero-carbon sources. However within the nearer time period, machine studying presents us actionable alternatives to cut back buildings’ carbon footprints.

Precision Agriculture

Agriculture is a significant driver of local weather change, accounting for between 10% and 15% of the world’s greenhouse fuel emissions.

Trendy agriculture is resource-intensive and wasteful. As an illustration, over 200 million tons of fertilizer are used for farming yearly, thousands and thousands of that are wasted because of imprecise and extreme software. It is a main downside for local weather change: fertilizer alone is accountable for 2.5% of all greenhouse fuel emissions. Worse, the greenhouse fuel that fertilizers generate, nitrous oxide, is very dangerous: pound for pound, it warms the environment about 300 instances greater than does carbon dioxide.

An enormous alternative exists to use digital applied sciences to make agriculture extra environment friendly, decreasing its carbon footprint whereas growing meals yields.

Precision agriculture is the apply of optimizing crop inputs (e.g., fertilizer, water, pesticides) on a focused, localized foundation, generally even plant by plant, quite than indiscriminately treating all crops the identical throughout total fields and farms. It would play a central function in making agriculture extra sustainable within the years forward.

In accordance with the World Financial Discussion board, if 15% to 25% of farms adopted precision agriculture strategies, greenhouse fuel emissions may very well be diminished by 10% and water use may very well be diminished by 20%, all whereas growing farming yields by 15%.

AI startups are enjoying a key function in making precision agriculture a actuality on farms around the globe.

One method is to use laptop imaginative and prescient to aerial imagery to be able to give farmers real-time insights about easy methods to optimally deploy sources on their farms: the place to use kind of fertilizer, the place to repair leaking irrigation pipes, and so forth. The attraction of this enterprise mannequin is that it’s software-based and thus capital-efficient and scalable. Startups pursuing this technique embody Ceres, Hummingbird Applied sciences, Gamaya and Prospera (which was acquired for $300 million simply final month).

Semios and Arable are two well-funded precision agriculture startups that use stationary on-the-ground {hardware} sensors to allow extra exact crop administration. Semios says it has put in over 2 million sensors on farms so far, from which it collects over 500 million information factors on daily basis.

A ultimate set of rivals, together with Bay Space-based startups FarmWise and Bear Flag Robotics, is deploying bodily robots on farms to be able to extra straight perform AI-guided precision farming. FarmWise’s preliminary focus is on weeding, with a longer-term imaginative and prescient to make use of its robots to allow precision agriculture throughout a broad spectrum of farming actions. Bear Flag Robotics has developed an autonomous tractor service.

You will need to observe that many of those startups don’t explicitly place themselves as “local weather expertise” firms. Precision agriculture applied sciences get market adoption at the start as a result of they make farms extra productive and environment friendly, saving prices and boosting output. The truth that in addition they drive decarbonization in one of many world’s largest carbon-emitting sectors is a fortuitous facet profit. As with HVAC optimization, mentioned above, startups that align emissions reductions with financial worth creation for patrons are finest positioned to succeed.

Anticipate to see a number of massive firms in-built precision agriculture within the years forward.

Renewables And The Grid

Producing zero-carbon electrical energy is on the very coronary heart of the battle towards local weather change. As Invoice Gates put it in his most up-to-date ebook: “If a genie provided me one want, a single breakthrough in only one exercise that drives local weather change, I’d decide making electrical energy: It’s going to play a giant function in decarbonizing different elements of the bodily financial system.”

The elemental breakthroughs wanted to make considerable zero-carbon electrical energy a actuality—higher vitality storage, next-generation nuclear fission, viable nuclear fusion—are at the start bodily engineering challenges. As we speak’s synthetic intelligence can not function a silver bullet to supply these primary advances in physics and chemistry.

But there are numerous methods during which machine studying can assist enhance immediately’s electrical energy methods and transfer us towards a renewables-centric future.

The ability grid is among the most complicated methods that people have ever constructed. As a result of electrical energy can’t be effectively saved at scale, the grid should repeatedly steadiness provide and demand in real-time. Machine studying can assist automate and optimize this complicated system, enabling grid operators to extra precisely forecast electrical energy flows and to get rid of inefficiencies that result in larger carbon emissions.

Winnipeg-based Invenia and Y Combinator alum Gaiascope are two attention-grabbing startups making use of AI to foretell and optimize electrical energy grid dynamics.

In an analogous vein, Gridware makes use of edge AI and telemetry sensors to foretell and detect faults within the grid’s bodily infrastructure in real-time, decreasing the chance of fires and different systemic failures.

One other participant making use of AI to assist decarbonize the grid is Raptor Maps, a pc vision-based platform that facilitates the deployment and administration of photo voltaic vitality property utilizing information from drones, satellites and on-the-ground sensors.

Synthetic intelligence may also contribute to the proliferation of renewable vitality by means of improved supplies science. As an illustration, researchers are making use of machine studying to assist uncover new compounds that may harness the solar’s energy extra successfully than immediately’s photovoltaics.

One promising early-stage startup utilizing AI to allow the invention of latest supplies is Israel-based Supplies Zone.


One of the seen and damaging reminders of our warming planet is the alarming improve in wildfires lately, from California to Australia.

2020 was the worst wildfire yr in recorded historical past in California, eclipsing the earlier file set in 2018. Over 4% of the state burned, leading to greater than $12 billion in losses.

As local weather change makes the planet hotter and drier, we face the prospect of devastating wildfires turning into the brand new regular, yr in and yr out. It’s crucial that we do what we will to mitigate this local weather danger. AI can assist.

Pano AI and Fion Applied sciences are two younger startups which can be making use of machine studying to bolster firefighting efforts. Their options use laptop imaginative and prescient to establish the place fireplace danger is biggest forward of time (so that fireplace professionals can take preventive measures), to detect fires instantly once they begin (in order that firefighters can swiftly reply earlier than the fireplace will get massive) and to foretell how fires will unfold (to be able to help firefighting efforts in real-time).

Firemaps, in the meantime, is constructing a product to assist particular person households take steps to guard themselves from fires. Primarily based on a buyer’s residence deal with, Firemaps analyzes satellite tv for pc imagery and different fireplace danger information to routinely generate a personalised fireplace protection plan for the property, together with actions like brush removing, gasoline breaks, and residential hardening.


Synthetic intelligence is a general-purpose expertise with infinite potential use circumstances. There’s maybe no AI software that issues extra for humanity than decarbonizing the environment and slowing local weather change. The chance for financial and societal worth creation is just about unbounded.

It’s arduous to think about a extra worthy discipline for AI entrepreneurs, researchers and operators to dedicate themselves to within the many years forward.

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